Model release

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BIAS.md

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+|Field:|Response:|
+|:---:|:---:|
+|Participation considerations from adversely impacted groups (protected classes) in model design and testing:|None|
+|Measures taken to mitigate against unwanted bias:|None|

EXPLAINABILITY.md

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+| Field: | Response: |
+| :---- | :---- |
+| Intended Application(s) & Domain(s): | Text generation, reasoning, summarization, and question answering.  |
+| Model Type: | Text-to-text transformer |
+| Intended Users: | This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency. |
+| Output: | Text String(s) |
+| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |
+| Technical Limitations: | The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs. The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text. |
+| Verified to have met prescribed quality standards? | Yes |
+| Performance Metrics: | Accuracy, Throughput, and user-side throughput |
+| Potential Known Risks: | The model was optimized explicitly for instruction following and as such is susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. The model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources -- either directly or indirectly by retrieval (e.g. via visiting a website) -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place.The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Model output should be appropriately escaped before viewing or other processing.|
+| End User License Agreement: | Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama. |

PRIVACY.md

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+|Field:|Response:|
+|:---:|:---:|
+|Generatable or Reverse engineerable personally-identifiable information?|None|
+|Was consent obtained for any personal data used?|None Known|
+|Personal data used to create this model?|None Known|
+|How often is dataset reviewed?|Before Release|
+|Is there provenance for all datasets used in training?|Yes|
+|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|
+|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|

README.md

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+---
+library_name: transformers
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - nvidia
+  - llama-3
+  - pytorch
+---
+
+# Llama-3.3-Nemotron-Super-49B-v1.5
+
+![image](./accuracy_chart.png)
+
+## Model Overview 
+
+Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.
+
+Llama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to [this paper](https://arxiv.org/abs/2411.19146)
+
+The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.
+
+This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here: 
+- [Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1)
+- [Llama-3.1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1)
+
+This model is ready for commercial use. 
+
+## License/Terms of Use
+
+GOVERNING TERMS: Your use of this model is governed by the [NVIDIA Open Model License.](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.
+
+**Model Developer:** NVIDIA
+
+**Model Dates:** Trained between November 2024 and July 2025
+
+**Data Freshness:**  The pretraining data has a cutoff of 2023 per Meta Llama 3.3 70B
+
+## Deployment Geography
+Global
+
+### Use Case: <br>
+Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. <br>
+
+### Release Date:  <br>
+- Hugging Face 7/25/2025 via [Llama-3_3-Nemotron-Super-49B-v1_5](https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5)
+- build.nvidia.com 7/25/2025 [Llama-3_3-Nemotron-Super-49B-v1_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5)
+
+## References
+
+* [\[2505.00949\] Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949)
+* [\[2502.00203\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203)  
+* [\[2411.19146\]Puzzle: Distillation-Based NAS for Inference-Optimized LLMs](https://arxiv.org/abs/2411.19146)  
+
+## Model Architecture
+
+**Architecture Type:** Dense decoder-only Transformer model
+
+**Network Architecture:** Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS)
+
+The model is a derivative of Meta’s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following: 
+
+Skip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer.
+Variable FFN: The expansion/compression ratio in the FFN layer is different between blocks. 
+
+We utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use-cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets - FineWeb, Buzz-V1.2 and Dolma.
+
+## Intended use
+
+Llama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. 
+
+## Input
+- **Input Type:** Text
+- **Input Format:** String
+- **Input Parameters:** One-Dimensional (1D)
+- **Other Properties Related to Input:** Context length up to 131,072 tokens
+
+## Output
+- **Output Type:** Text
+- **Output Format:** String
+- **Output Parameters:** One-Dimensional (1D)
+- **Other Properties Related to Output:** Context length up to 131,072 tokens
+
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
+
+## Model Version
+1.5 (07/25/2025)
+
+## Software Integration
+- **Runtime Engine:** Transformers
+
+- **Recommended Hardware Microarchitecture Compatibility:** 
+  - NVIDIA Ampere 
+  - NVIDIA Hopper 
+- **Preferred Operating System(s):** Linux 
+
+## Quick Start and Usage Recommendations:
+
+1. By default (empty system prompt) the model will respond in reasoning ON mode. Setting `/no_think` in the system prompt will enable reasoning OFF mode.
+2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode
+3. We recommend using greedy decoding for Reasoning OFF mode
+
+You can try this model out through the preview API, using this link: [Llama-3_3-Nemotron-Super-49B-v1_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5).
+
+## Use It with vLLM
+
+```pip install vllm==0.9.2```
+
+An example on how to serve with vLLM:
+
+```console
+$ python3 -m vllm.entrypoints.openai.api_server \
+  --model "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5" \
+  --trust-remote-code \
+  --seed=1 \
+  --host="0.0.0.0" \
+  --port=5000 \
+  --served-model-name "Llama-3_3-Nemotron-Super-49B-v1_5" \
+  --tensor-parallel-size=8 \
+  --max-model-len=65536 \
+  --gpu-memory-utilization 0.95 \
+  --enforce-eager
+```
+
+### Running a vLLM Server with Tool-call Support
+To enable tool calling usage with this model, we provide a tool parser in the repository. Here is an example on how to use it:
+
+
+```console
+$ git clone https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5
+
+$ conda create -n vllm python=3.12 -y
+$ conda activate vllm
+$ pip install vllm==0.9.2
+
+$ python3 -m vllm.entrypoints.openai.api_server \
+  --model Llama-3_3-Nemotron-Super-49B-v1_5 \
+  --trust-remote-code \
+  --seed=1 \
+  --host="0.0.0.0" \
+  --port=5000 \
+  --served-model-name "Llama-3_3-Nemotron-Super-49B-v1_5" \
+  --tensor-parallel-size=8 \
+  --max-model-len=65536 \
+  --gpu-memory-utilization 0.95 \
+  --enforce-eager \
+  --enable-auto-tool-choice \
+  --tool-parser-plugin "Llama-3_3-Nemotron-Super-49B-v1_5/llama_nemotron_toolcall_parser_no_streaming.py" \
+  --tool-call-parser "llama_nemotron_json"
+```
+
+After launching a vLLM server, you can call the server with tool-call support using a Python script like below.
+
+```python
+from openai import OpenAI
+client = OpenAI(
+    base_url="http://0.0.0.0:5000/v1",
+    api_key="dummy",
+)
+completion = client.chat.completions.create(
+    model="Llama-3_3-Nemotron-Super-49B-v1_5",
+    messages=[
+        {"role": "system", "content": ""},
+        {"role": "user", "content": "My bill is $100. What will be the amount for 18% tip?"}
+    ],
+    tools=[
+        {
+            "type": "function",
+            "function": {
+                "name": "calculate_tip",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "bill_total": {
+                            "type": "integer",
+                            "description": "The total amount of the bill"
+                        },
+                        "tip_percentage": {
+                            "type": "integer",
+                            "description": "The percentage of tip to be applied"
+                        }
+                    },
+                    "required": ["bill_total", "tip_percentage"]
+                }
+            }
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "convert_currency",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "amount": {
+                            "type": "integer",
+                            "description": "The amount to be converted"
+                        },
+                        "from_currency": {
+                            "type": "string",
+                            "description": "The currency code to convert from"
+                        },
+                        "to_currency": {
+                            "type": "string",
+                            "description": "The currency code to convert to"
+                        }
+                    },
+                    "required": ["from_currency", "amount", "to_currency"]
+                }
+            }
+        }
+    ],
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=32768,
+    stream=False
+)
+print(completion.choices[0].message.content)
+'''
+<think>
+Okay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.
+</think>
+'''
+print(completion.choices[0].message.tool_calls)
+'''
+[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]
+'''
+```
+
+## Training and Evaluation Datasets
+
+## Training Datasets
+
+A large variety of training data was used for the knowledge distillation phase before post-training pipeline, 3 of which included: FineWeb, Buzz-V1.2, and Dolma.
+
+The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model. 
+
+Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes. 
+
+NVIDIA will be releasing the post-training dataset in the coming weeks.
+
+**Data Collection for Training Datasets:**
+Hybrid: Automated, Human, Synthetic
+
+**Data Labeling for Training Datasets:**
+Hybrid: Automated, Human, Synthetic
+
+## Evaluation Datasets 
+
+We used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1.5. 
+
+Data Collection for Evaluation Datasets:
+- Hybrid: Human. Synthetic
+
+Data Labeling for Evaluation Datasets:
+- Hybrid: Human, Synthetic, Automatic
+
+## Evaluation Results
+We evaluate the model using temperature=`0.6`, top_p=`0.95`, and 64k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.
+
+### MATH500
+
+| Reasoning Mode | pass@1 (avg. over 4 runs) |
+|--------------|------------|
+| Reasoning On | 97.4 |
+
+### AIME 2024
+
+| Reasoning Mode | pass@1 (avg. over 16 runs) |
+|--------------|------------|
+| Reasoning On | 87.5 |
+
+### AIME 2025
+
+| Reasoning Mode | pass@1 (avg. over 16 runs) |
+|--------------|------------|
+| Reasoning On | 82.71 |
+
+### GPQA
+
+| Reasoning Mode | pass@1 (avg. over 4 runs) |
+|--------------|------------|
+| Reasoning On | 71.97 |
+
+### LiveCodeBench 24.10-25.02
+
+| Reasoning Mode | pass@1 (avg. over 4 runs) |
+|--------------|------------|
+| Reasoning On | 73.58 |
+
+### BFCL v3
+
+| Reasoning Mode | pass@1 (avg. over 2 runs) |
+|--------------|------------|
+| Reasoning On | 71.75 |
+
+### IFEval
+
+| Reasoning Mode | Strict:Instruction |
+|--------------|------------|
+| Reasoning On | 88.61 | 
+
+### ArenaHard
+
+| Reasoning Mode | pass@1 (avg. over 1 runs) |
+|--------------|------------|
+| Reasoning On | 92.0 | 
+
+### Humanity's Last Exam (Text-Only Subset)
+
+| Reasoning Mode | pass@1 (avg. over 1 runs) |
+|--------------|------------|
+| Reasoning On | 7.64 | 
+
+### MMLU Pro (CoT)
+
+| Reasoning Mode | pass@1 (avg. over 1 runs) |
+|--------------|------------|
+| Reasoning On | 79.53 | 
+
+All evaluations were done using the [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills) repository.
+
+## Inference:
+
+**Engine:**
+- Transformers
+
+**Test Hardware:**
+- 2x NVIDIA H100-80GB
+- 2x NVIDIA A100-80GB GPUs
+
+## Ethical Considerations:
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. 
+
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY&SECURITY.md), and [Privacy](./PRIVACY.md) Subcards.
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+## Citation
+
+```
+@misc{bercovich2025llamanemotronefficientreasoningmodels,
+      title={Llama-Nemotron: Efficient Reasoning Models}, 
+      author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk},
+      year={2025},
+      eprint={2505.00949},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.00949}, 
+}
+```

SAFETY&SECURITY.md

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+|Field:|Response:|
+|:---:|:---:|
+|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|
+|Describe life critical application (if present):|None Known (please see referenced Known Risks in the Explainability subcard).|
+|Use Case Restrictions:|Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.|
+|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face and NGC, and may become available on cloud providers' model catalog.|

block_config.py

@@ -0,0 +1,118 @@
+import dataclasses
+import json
+import warnings
+from dataclasses import dataclass, MISSING
+from functools import partial
+from typing import Optional, Any
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class JsonComparable:
+    def to_json(self) -> str:
+        return json.dumps(dataclasses.asdict(self))
+
+    def __eq__(self, other: "JsonComparable") -> bool:
+        return self.to_json() == other.to_json()
+
+    def __hash__(self) -> int:
+        return hash(self.to_json())
+
+    def __lt__(self, other: "JsonComparable") -> bool:
+        return self.to_json() < other.to_json()
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class SubblockConfig(JsonComparable):
+    no_op: bool = False
+    replace_with_linear: bool = False
+    sparsify: Optional[list[str]] = None
+
+    def __post_init__(self):
+        assert not (self.no_op and self.replace_with_linear)
+
+    def _force_setattr(self, name: str, value: Any) -> None:
+        """
+        Set an attribute even in frozen dataclasses.
+        Use only inside __post_init__!
+        """
+        object.__setattr__(self, name, value)
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class AttentionConfig(SubblockConfig):
+    n_heads_in_group: Optional[int] = None
+    window_length: Optional[int] = None
+    num_sink_tokens: Optional[int] = None
+    use_prefill_window_in_sink_attention: bool = False
+    unshifted_sink: bool = False
+
+    def __post_init__(self):
+        super().__post_init__()
+        assert not (self.no_op and self.replace_with_linear)
+
+        if self.no_op or self.replace_with_linear:
+            for irrelevant_att in ["n_heads_in_group", "window_length", "num_sink_tokens"]:
+                self._force_setattr(irrelevant_att, None)
+        else:
+            assert self.n_heads_in_group is not None
+
+        if self.is_sink:
+            assert not (self.unshifted_sink and self.use_prefill_window_in_sink_attention), \
+                ("Unshifted sink uses its own kind of explicit masking, not standard window. "
+                 "Set use_prefill_window_in_sink_attention to False.")
+            assert not (self.num_sink_tokens == 0 and not self.unshifted_sink), \
+                "Fake sink attention with 0 sink tokens is only supported with unshifted_sink=True"
+
+    @property
+    def prefill_sliding_window(self) -> Optional[int]:
+        if self.window_length is not None:
+            if not self.is_sink or self.use_prefill_window_in_sink_attention:
+                return self.window_length
+        return None
+
+    @property
+    def is_sliding(self) -> bool:
+        return self.prefill_sliding_window is not None
+
+    @property
+    def is_sink(self) -> bool:
+        return (
+                (self.window_length is not None)
+                and
+                (self.num_sink_tokens is not None)
+        )
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class FFNConfig(SubblockConfig):
+    ffn_mult: Optional[float] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.no_op or self.replace_with_linear:
+            self._force_setattr("ffn_mult", None)
+        else:
+            assert self.ffn_mult is not None
+            self._force_setattr("ffn_mult", round(self.ffn_mult, 6))
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class BlockConfig(JsonComparable):
+    attention: AttentionConfig = MISSING
+    ffn: FFNConfig = MISSING
+
+    def __post_init__(self):
+        """
+        Init subblock dataclasses from dicts
+        """
+        for subblock_name in dataclasses.fields(self):
+            subblock_config = getattr(self, subblock_name.name)
+            if isinstance(subblock_config, dict):
+                subblock_fields = [field.name for field in dataclasses.fields(subblock_name.type)]
+                unsupported_fields = [field_name for field_name in subblock_config.keys()
+                                      if field_name not in subblock_fields]
+                if len(unsupported_fields) > 0:
+                    warnings.warn(f"Removed unsupported fields {unsupported_fields} from {subblock_name.type.__name__}")
+                subblock_config = {k: v for k, v in subblock_config.items() if k not in unsupported_fields}
+                object.__setattr__(self, subblock_name.name,
+                                   subblock_name.type(**subblock_config))  # __setattr__ to overcome frozen=True

config.json

@@ -0,0 +1,1485 @@
+{
+  "_name_or_path": "llama_nemotron_super_v1_5",
+  "architectures": [
+    "DeciLMForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_decilm.DeciLMConfig",
+    "AutoModelForCausalLM": "modeling_decilm.DeciLMForCausalLM"
+  },
+  "block_configs": [
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 2.625,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": null,
+        "no_op": true,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 2.625,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": null,
+        "no_op": true,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 2.625,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
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+        "window_length": null
+      },
+      "ffn": {
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": null,
+        "no_op": true,
+        "num_sink_tokens": null,
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+        "sparsify": null,
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+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 3.28125,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
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+      },
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+        "no_op": false,
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+      }
+    },
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+        "sparsify": null,
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+        "use_prefill_window_in_sink_attention": false,
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+      },
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
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+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
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+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
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+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
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+      }
+    },
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
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+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
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+        "use_prefill_window_in_sink_attention": false,
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+      },
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+        "no_op": false,
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+        "sparsify": null
+      }
+    },
+    {
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+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
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+      },
+      "ffn": {
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
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+        "sparsify": null,
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+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
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+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
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+      },
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+        "sparsify": null
+      }
+    },
+    {
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+        "window_length": null
+      },
+      "ffn": {
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
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+      }
+    },
+    {
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+        "sparsify": null
+      }
+    },
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+        "no_op": false,
+        "num_sink_tokens": null,
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+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    }
+  ],
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": null,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "nemotron-nas",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 16.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

configuration_decilm.py

@@ -0,0 +1,65 @@
+# coding=utf-8
+# Copyright 2024 Nvidia Corporation. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import dataclasses
+import warnings
+from typing import Dict, Any
+
+from transformers.utils import is_flash_attn_2_available
+
+from .block_config import BlockConfig
+from .transformers_4_44_2__configuration_llama import LlamaConfig
+from .transformers_4_44_2__modeling_rope_utils import \
+    rope_config_validation  # fake import to make AutoConfig infer the dependency
+
+rope_config_validation  # this line is here to make sure that auto-formatting doesn't remove the import
+
+
+class DeciLMConfig(LlamaConfig):
+    model_type = "nemotron_nas"
+
+    def __init__(
+            self,
+            block_configs: list[dict] | list[BlockConfig] = None,
+            **kwargs,
+    ):
+        attn_implementation = kwargs.pop("attn_implementation", None)
+        if attn_implementation is None and is_flash_attn_2_available():
+            attn_implementation = "flash_attention_2"
+
+        if block_configs is not None:
+            if isinstance(block_configs[0], dict):
+                block_configs = [BlockConfig(**conf) for conf in block_configs]
+
+            using_unshifted_sink = any([block_config.attention.unshifted_sink for block_config in block_configs])
+            if using_unshifted_sink and attn_implementation != "eager":
+                warnings.warn("Forcing attn_implementation='eager' since some attention layers use unshifted sink")
+                attn_implementation = "eager"
+
+        super().__init__(attn_implementation=attn_implementation, **kwargs)
+
+        self.intermediate_size = None
+        self.num_key_value_heads = None
+
+        if block_configs is not None:
+            assert len(block_configs) == self.num_hidden_layers
+
+        self.block_configs: list[BlockConfig] = block_configs
+
+    def to_dict(self) -> Dict[str, Any]:
+        self_dict = super().to_dict()
+        if self.block_configs is not None:
+            self_dict["block_configs"] = [dataclasses.asdict(conf) for conf in self.block_configs]
+        return self_dict

generation_config.json

@@ -0,0 +1,11 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "transformers_version": "4.48.3"
+}

llama_nemotron_toolcall_parser_no_streaming.py

@@ -0,0 +1,468 @@
+import ast
+import json
+import re
+from collections.abc import Sequence
+from typing import Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    DeltaFunctionCall, DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser,
+    ToolParserManager,
+)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("llama_nemotron_xml")
+class LlamaNemotronXMLToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1  # Potentially for streaming
+        self.streamed_args_for_tool: list[str] = [] # Potentially for streaming
+
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+
+        # Regex to find full <tool_call>...</tool_call> blocks and capture their content
+        self.tool_call_block_regex = re.compile(r"<tool_call>(.*?)</tool_call>", re.DOTALL)
+        # Regex to find <tool>...</tool> within a tool_call block content
+        self.name_regex = re.compile(r"<tool>(.*?)</tool>", re.DOTALL)
+        # Regex to find <key>value</key> pairs within the tool_call block content (excluding <tool> tags)
+        self.param_regex = re.compile(r"<([^/>\s]+)>(.*?)</\1>", re.DOTALL)
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        tool_call_start_index = model_output.find(self.tool_call_start_token)
+
+        if tool_call_start_index == -1:
+            return ExtractedToolCallInformation(
+                tools_called=False,
+                tool_calls=[],
+                content=model_output,
+            )
+        
+        content = model_output[:tool_call_start_index].strip()
+        tool_calls_str_content = model_output[tool_call_start_index:]
+
+        parsed_tool_calls = []
+        
+        try:
+            # Find all occurrences of <tool_call>...</tool_call>
+            xml_tool_call_contents = self.tool_call_block_regex.findall(tool_calls_str_content)
+
+            for tool_content_str in xml_tool_call_contents:
+                name_match = self.name_regex.search(tool_content_str)
+                if not name_match:
+                    logger.warning(f"Could not find tool name in XML block: {tool_content_str}")
+                    continue
+                tool_name = name_match.group(1).strip()
+
+                parsed_arguments = {}
+                
+                # Find all parameter tags in the tool_call content, excluding the <tool> tag
+                param_matches = self.param_regex.finditer(tool_content_str)
+                
+                for match in param_matches:
+                    param_name = match.group(1).strip()
+                    param_value_str = match.group(2).strip()
+                    
+                    # Skip the <tool> tag since it's not a parameter
+                    if param_name == "tool":
+                        continue
+                    
+                    target_type = None
+                    # Try to get type from request.tools schema
+                    if request.tools:
+                        for tool_def in request.tools:
+                            if tool_def.function.name == tool_name:
+                                if tool_def.function.parameters and \
+                                   isinstance(tool_def.function.parameters, dict) and \
+                                   "properties" in tool_def.function.parameters and \
+                                   isinstance(tool_def.function.parameters["properties"], dict) and \
+                                   param_name in tool_def.function.parameters["properties"] and \
+                                   isinstance(tool_def.function.parameters["properties"][param_name], dict):
+                                    target_type = tool_def.function.parameters["properties"][param_name].get("type")
+                                break
+                    
+                    typed_param_value = param_value_str # Default to string
+                    if target_type:
+                        try:
+                            if target_type == "string":
+                                typed_param_value = param_value_str
+                            elif target_type == "integer":
+                                typed_param_value = int(param_value_str)
+                            elif target_type == "number":
+                                typed_param_value = float(param_value_str)
+                            elif target_type == "boolean":
+                                typed_param_value = param_value_str.lower() == 'true'
+                            elif target_type in ["object", "array"]:
+                                try:
+                                    typed_param_value = json.loads(param_value_str)
+                                except json.JSONDecodeError:
+                                    # Fallback for non-strict JSON like Python dict/list string
+                                    typed_param_value = ast.literal_eval(param_value_str)
+                            else: # Unknown type, keep as string
+                                typed_param_value = param_value_str
+                        except (ValueError, SyntaxError, json.JSONDecodeError) as e:
+                            logger.warning(
+                                f"Could not convert param '{param_name}' with value '{param_value_str}' "
+                                f"to type '{target_type}'. Error: {e}. Using string value."
+                            )
+                            typed_param_value = param_value_str
+                    else: # No schema type, try ast.literal_eval
+                        try:
+                            # For values like "true", "123", "['a', 'b']"
+                            # ast.literal_eval('some_string_without_quotes') will raise SyntaxError
+                            if (param_value_str.startswith("'") and param_value_str.endswith("'")) or \
+                               (param_value_str.startswith('"') and param_value_str.endswith('"')) or \
+                               (param_value_str.startswith('[') and param_value_str.endswith(']')) or \
+                               (param_value_str.startswith('{') and param_value_str.endswith('}')) or \
+                               param_value_str.lower() in ['true', 'false', 'none'] or \
+                               param_value_str.replace('.', '', 1).isdigit() or \
+                               (param_value_str.startswith('-') and param_value_str[1:].replace('.', '', 1).isdigit()):
+                                typed_param_value = ast.literal_eval(param_value_str)
+                            else: # It's likely a plain string not meant for ast.literal_eval
+                                typed_param_value = param_value_str
+                        except (ValueError, SyntaxError):
+                            typed_param_value = param_value_str # Keep as string if ast.literal_eval fails
+
+                    parsed_arguments[param_name] = typed_param_value
+                
+                parsed_tool_calls.append(ToolCall(
+                    id=f"call_{random_uuid()}",
+                    type="function",
+                    function=FunctionCall(
+                        name=tool_name,
+                        arguments=json.dumps(parsed_arguments, ensure_ascii=False),
+                    ),
+                ))
+
+            return ExtractedToolCallInformation(
+                tools_called=len(parsed_tool_calls) > 0,
+                tool_calls=parsed_tool_calls,
+                content=content if content else None,
+            )
+
+        except Exception:
+            logger.exception(f"Error in extracting XML tool call from response. Response: {model_output}")
+            # Fallback to original model output if parsing fails catastrophically
+            return ExtractedToolCallInformation(
+                tools_called=False,
+                tool_calls=[],
+                content=model_output,
+            )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        raise NotImplementedError("Tool calling is not supported in streaming mode!")
+
+
+@ToolParserManager.register_module("llama_nemotron_json")
+class LlamaNemotronJSONToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        self.tool_call_start_token: str = "<TOOLCALL>"
+        self.tool_call_end_token: str = "</TOOLCALL>"
+
+        self.tool_call_regex = re.compile(r"<TOOLCALL>(.*?)</TOOLCALL>", re.DOTALL)
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False,
+                tool_calls=[],
+                content=model_output,
+            )
+
+        else:
+
+            try:
+                str_tool_calls = self.tool_call_regex.findall(model_output)[0].strip()
+                if not str_tool_calls.startswith("["):
+                    str_tool_calls = "[" + str_tool_calls
+                if not str_tool_calls.endswith("]"):
+                    str_tool_calls = "]" + str_tool_calls
+                json_tool_calls = json.loads(str_tool_calls)
+                tool_calls = []
+                for tool_call in json_tool_calls:
+                    try:
+                        tool_calls.append(ToolCall(
+                            type="function",
+                            function=FunctionCall(
+                                name=tool_call["name"],
+                                arguments=json.dumps(tool_call["arguments"], ensure_ascii=False) \
+                                    if isinstance(tool_call["arguments"], dict) else tool_call["arguments"],
+                            ),
+                        ))
+                    except:
+                        continue
+
+                content = model_output[:model_output.rfind(self.tool_call_start_token)]
+
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None,
+                )
+
+            except Exception:
+                logger.exception(f"Error in extracting tool call from response. Response: {model_output}")
+                return ExtractedToolCallInformation(
+                    tools_called=False,
+                    tool_calls=[],
+                    content=model_output,
+                )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        raise NotImplementedError("Tool calling is not supported in streaming mode!")
+
+
+@ToolParserManager.register_module("llama_nemotron_pythonic")
+class LlamaNemotronPythonicToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        self.tool_call_start_token: str = "<TOOLCALL>"
+        self.tool_call_end_token: str = "</TOOLCALL>"
+
+        self.tool_call_regex = re.compile(r"<TOOLCALL>(.*?)</TOOLCALL>", re.DOTALL)
+        # Regex to parse pythonic function calls: function_name(arg1="value1", arg2=123, arg3=True)
+        self.function_call_regex = re.compile(r"(\w+)\((.*?)\)$", re.DOTALL)
+
+    def parse_function_arguments(self, args_str: str) -> dict:
+        """Parse pythonic function arguments string into a dictionary"""
+        if not args_str.strip():
+            return {}
+        
+        # Use ast.parse to safely parse the function call arguments
+        # We'll construct a temporary function call and parse it
+        try:
+            # Create a dummy function call to parse arguments
+            dummy_code = f"dummy_func({args_str})"
+            parsed = ast.parse(dummy_code, mode='eval')
+            
+            # Extract arguments from the AST
+            call_node = parsed.body
+            if not isinstance(call_node, ast.Call):
+                return {}
+            
+            arguments = {}
+            
+            # Handle keyword arguments
+            for keyword in call_node.keywords:
+                if keyword.arg is None:  # **kwargs
+                    continue
+                    
+                # Convert AST value to Python value
+                try:
+                    value = ast.literal_eval(keyword.value)
+                    arguments[keyword.arg] = value
+                except (ValueError, TypeError):
+                    # If literal_eval fails, try to get the raw value
+                    if isinstance(keyword.value, ast.Name):
+                        arguments[keyword.arg] = keyword.value.id
+                    elif isinstance(keyword.value, ast.Constant):
+                        arguments[keyword.arg] = keyword.value.value
+                    else:
+                        # Fallback: convert to string
+                        arguments[keyword.arg] = ast.unparse(keyword.value)
+            
+            # Handle positional arguments (less common in tool calls but supported)
+            for i, arg in enumerate(call_node.args):
+                try:
+                    value = ast.literal_eval(arg)
+                    arguments[f"arg_{i}"] = value
+                except (ValueError, TypeError):
+                    if isinstance(arg, ast.Name):
+                        arguments[f"arg_{i}"] = arg.id
+                    elif isinstance(arg, ast.Constant):
+                        arguments[f"arg_{i}"] = arg.value
+                    else:
+                        arguments[f"arg_{i}"] = ast.unparse(arg)
+            
+            return arguments
+            
+        except (SyntaxError, ValueError) as e:
+            logger.warning(f"Failed to parse function arguments '{args_str}': {e}")
+            return {}
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False,
+                tool_calls=[],
+                content=model_output,
+            )
+
+        tool_call_start_index = model_output.find(self.tool_call_start_token)
+        content = model_output[:tool_call_start_index].strip()
+        
+        try:
+            # Extract content between <TOOLCALL> tags
+            tool_call_matches = self.tool_call_regex.findall(model_output)
+            if not tool_call_matches:
+                return ExtractedToolCallInformation(
+                    tools_called=False,
+                    tool_calls=[],
+                    content=model_output,
+                )
+            
+            tool_calls_content = tool_call_matches[0].strip()
+            
+            # Split by lines to get individual function calls
+            function_lines = [line.strip() for line in tool_calls_content.split('\n') if line.strip()]
+            
+            parsed_tool_calls = []
+            
+            for func_line in function_lines:
+                # Parse each function call
+                match = self.function_call_regex.match(func_line)
+                if not match:
+                    logger.warning(f"Could not parse function call: {func_line}")
+                    continue
+                
+                function_name = match.group(1)
+                args_str = match.group(2)
+                
+                # Parse arguments
+                parsed_arguments = self.parse_function_arguments(args_str)
+                
+                # Apply type conversion based on schema if available
+                if request.tools:
+                    for tool_def in request.tools:
+                        if tool_def.function.name == function_name:
+                            schema_properties = {}
+                            if (tool_def.function.parameters and 
+                                isinstance(tool_def.function.parameters, dict) and 
+                                "properties" in tool_def.function.parameters and 
+                                isinstance(tool_def.function.parameters["properties"], dict)):
+                                schema_properties = tool_def.function.parameters["properties"]
+                            
+                            # Convert arguments based on schema types
+                            for arg_name, arg_value in parsed_arguments.items():
+                                if arg_name in schema_properties:
+                                    param_info = schema_properties[arg_name]
+                                    target_type = param_info.get("type")
+                                    
+                                    try:
+                                        if target_type == "string" and not isinstance(arg_value, str):
+                                            parsed_arguments[arg_name] = str(arg_value)
+                                        elif target_type == "integer" and not isinstance(arg_value, int):
+                                            parsed_arguments[arg_name] = int(arg_value)
+                                        elif target_type == "number" and not isinstance(arg_value, (int, float)):
+                                            parsed_arguments[arg_name] = float(arg_value)
+                                        elif target_type == "boolean" and not isinstance(arg_value, bool):
+                                            if isinstance(arg_value, str):
+                                                parsed_arguments[arg_name] = arg_value.lower() in ['true', '1', 'yes']
+                                            else:
+                                                parsed_arguments[arg_name] = bool(arg_value)
+                                        elif target_type in ["object", "array"]:
+                                            if isinstance(arg_value, str):
+                                                try:
+                                                    parsed_arguments[arg_name] = json.loads(arg_value)
+                                                except json.JSONDecodeError:
+                                                    # Keep as string if JSON parsing fails
+                                                    pass
+                                    except (ValueError, TypeError) as e:
+                                        logger.warning(f"Type conversion failed for {arg_name}: {e}")
+                                        # Keep original value if conversion fails
+                            break
+                
+                parsed_tool_calls.append(ToolCall(
+                    id=f"call_{random_uuid()}",
+                    type="function",
+                    function=FunctionCall(
+                        name=function_name,
+                        arguments=json.dumps(parsed_arguments, ensure_ascii=False),
+                    ),
+                ))
+
+            return ExtractedToolCallInformation(
+                tools_called=len(parsed_tool_calls) > 0,
+                tool_calls=parsed_tool_calls,
+                content=content if content else None,
+            )
+
+        except Exception:
+            logger.exception(f"Error in extracting pythonic tool call from response. Response: {model_output}")
+            return ExtractedToolCallInformation(
+                tools_called=False,
+                tool_calls=[],
+                content=model_output,
+            )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        raise NotImplementedError("Tool calling is not supported in streaming mode!")

model-00001-of-00021.safetensors

@@ -0,0 +1,3 @@
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{% set bos = \"<|begin_of_text|>\" %}{%- set enable_thinking = true -%}{% set system_start_header = \"<|start_header_id|>\" %}{% set system_end_header = \"<|end_header_id|>\n\n\" %}{% set start_header = \"<|start_header_id|>\" %}{% set end_header = \"<|end_header_id|>\n\n\" %}{% set eot = \"<|eot_id|>\" %}{% set system_token = \"system\" %}{% set user_token = \"user\" %}{% set assistant_token = \"assistant\" %}{% set tool_token = \"tool\" %}{{- bos ~ system_start_header ~ system_token ~ system_end_header -}}{%- if messages[0].role == 'system' and messages[0].content != '' -%}{%- set system_content = messages[0].content -%}{%- if '/no_think' in system_content -%}{%- set system_content = system_content.replace('/no_think', '')|trim -%}{%- set enable_thinking = false -%}{%- elif '/think' in system_content -%}{%- set system_content = system_content.replace('/think', '')|trim -%}{%- set enable_thinking = true -%}{%- endif -%}{{- system_content + '\n\n' -}}{%- endif -%}{%- if tools -%}{{- 'You can use the following tools to assist the user if required:\n<AVAILABLE_TOOLS>[' -}}{%- for tool in tools -%}{{- (tool.function if tool.function is defined else tool) | tojson -}}{{- ', ' if not loop.last else '' -}}{%- endfor -%}{{- ']</AVAILABLE_TOOLS>\n\nIf you decide to call any tool(s), use the following format:\n<TOOLCALL>[{{\"name\": \"tool_name1\", \"arguments\": \"tool_args1\"}}, {{\"name\": \"tool_name2\", \"arguments\": \"tool_args2\"}}]</TOOLCALL>\n\nResponse from tool(s) will be returned in this format:\n<TOOL_RESPONSE>[{{\"response\": \"tool_response1\"}}, {{\"response\": \"tool_response2\"}}]</TOOL_RESPONSE>\n\nBased on the results returned by the tool(s), you can call additional tools if needed, correct tool calls if any errors are found, or just respond with the answer to the user.' -}}{%- endif -%}{{- eot -}}{%- for message in messages -%}{%- if message.role == user_token -%}{{- start_header ~ user_token ~ end_header -}}{{ message.content -}}{{ eot -}}{%- elif message.role == assistant_token -%}{%- if '</think>' in message.content -%}{%- set content = message.content.split('</think>')[-1].lstrip() -%}{%- else -%}{%- set content = message.content -%}{%- endif -%}{{- start_header ~ assistant_token ~ end_header -}}{{ content -}}{%- if message.tool_calls -%}{{- '<TOOLCALL>[' -}}{%- for call in message.tool_calls -%}{%- set fn = call.function if call.function is defined else call -%}{{- '{\"name\": \"' + fn.name + '\", \"arguments\": ' -}}{%- if fn.arguments is string -%}{{- fn.arguments -}}{%- else -%}{{- fn.arguments | tojson -}}{%- endif -%}{{- '}' + (', ' if not loop.last else '') -}}{%- endfor -%}{{- ']</TOOLCALL>' -}}{%- endif -%}{{- eot -}}{%- elif message.role == tool_token -%}{%- if loop.first or (messages[loop.index0 - 1].role != tool_token) -%}{{- start_header ~ tool_token ~ end_header -}}{{ '<TOOL_RESPONSE>[' -}}{%- endif -%}{{- message.content -}}{{- ', ' if not loop.last and (messages[loop.index0 + 1].role == tool_token) else '' -}}{%- if loop.last or (messages[loop.index0 + 1].role != tool_token) -%}{{- ']</TOOL_RESPONSE>' -}}{{ eot -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- start_header ~ assistant_token ~ end_header -}}{%- if not enable_thinking -%}{{- '<think>\n\n</think>\n\n' -}}{%- endif -%}{%- endif -%}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}
+

transformers_4_44_2__configuration_llama.py

@@ -0,0 +1,203 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from .transformers_4_44_2__modeling_rope_utils import rope_config_validation
+
+
+class LlamaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+
+    ```python
+    >>> from transformers import LlamaModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = LlamaConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = LlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

transformers_4_44_2__modeling_rope_utils.py

@@ -0,0 +1,559 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional, Tuple
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_torch_available():
+    import torch
+
+
+def _compute_default_rope_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies according to the original RoPE implementation
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # Compute the inverse frequencies
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_linear_scaling_rope_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_linear_scaling_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        factor = config.rope_scaling["factor"]
+
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    # Then applies linear scaling to the frequencies.
+    # NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
+    # applying scaling to the inverse frequencies is equivalent.
+    inv_freq /= factor
+    return inv_freq, attention_factor
+
+
+def _compute_dynamic_ntk_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length, used to update the dynamic RoPE at inference time.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_dynamic_ntk_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+        max_position_embeddings = rope_kwargs["max_position_embeddings"]
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
+        max_position_embeddings = config.max_position_embeddings
+        factor = config.rope_scaling["factor"]
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # seq_len: default to max_position_embeddings, e.g. at init time
+    seq_len = seq_len if seq_len is not None and seq_len > max_position_embeddings else max_position_embeddings
+
+    # Compute the inverse frequencies
+    base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_yarn_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Please refer to the
+    [original paper](https://arxiv.org/abs/2309.00071)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # No need to keep BC with yarn, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            f"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got {rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+    max_position_embeddings = config.max_position_embeddings
+    factor = config.rope_scaling["factor"]
+
+    # Sets the attention factor as suggested in the paper
+    attention_factor = config.rope_scaling.get("attention_factor")
+    if attention_factor is None:
+        attention_factor = 0.1 * math.log(factor) + 1.0
+
+    # Optional config options
+    # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
+    beta_fast = config.rope_scaling.get("beta_fast") or 32
+    beta_slow = config.rope_scaling.get("beta_slow") or 1
+
+    # Compute the inverse frequencies
+    def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
+        """Inverse dimension formula to find the dimension based on the number of rotations"""
+        return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
+
+    def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
+        """Find dimension range bounds based on rotations"""
+        low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
+        high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
+        return max(low, 0), min(high, dim - 1)
+
+    def linear_ramp_factor(min, max, dim):
+        if min == max:
+            max += 0.001  # Prevent singularity
+
+        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+        ramp_func = torch.clamp(linear_func, 0, 1)
+        return ramp_func
+
+    # Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
+    # to expand the possible context length. In other words, interpolation = apply scaling factor.
+    pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
+    inv_freq_extrapolation = 1.0 / pos_freqs
+    inv_freq_interpolation = 1.0 / (factor * pos_freqs)
+
+    low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
+
+    # Get n-dimensional rotational scaling corrected for extrapolation
+    inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).float().to(device)
+    inv_freq = (
+        inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
+        + inv_freq_extrapolation * inv_freq_extrapolation_factor
+    )
+
+    return inv_freq, attention_factor
+
+
+def _compute_longrope_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with LongRoPE scaling. Please refer to the
+    [original implementation](https://github.com/microsoft/LongRoPE)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    # No need to keep BC with longrope, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got "
+            f"{rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+    long_factor = config.rope_scaling["long_factor"]
+    short_factor = config.rope_scaling["short_factor"]
+    factor = config.rope_scaling.get("factor")
+    attention_factor = config.rope_scaling.get("attention_factor")
+
+    # NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
+    # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
+    # values to compute the default attention scaling factor, instead of using `factor`.
+    if hasattr(config, "original_max_position_embeddings"):
+        max_position_embeddings = config.original_max_position_embeddings
+        expanded_max_position_embeddings = config.max_position_embeddings
+        factor = expanded_max_position_embeddings / max_position_embeddings
+    else:
+        max_position_embeddings = config.max_position_embeddings
+        expanded_max_position_embeddings = max_position_embeddings * factor
+
+    # Sets the attention factor as suggested in the paper
+    if attention_factor is None:
+        if factor <= 1.0:
+            attention_factor = 1.0
+        else:
+            attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
+
+    # Compute the inverse frequencies -- scaled based on the target sequence length
+    if expanded_max_position_embeddings > max_position_embeddings:
+        ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
+    else:
+        ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
+    inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
+    inv_freq = 1.0 / (ext_factors * base**inv_freq_shape)
+
+    return inv_freq, attention_factor
+
+
+def _compute_llama3_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies for llama 3.1.
+
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    factor = config.rope_scaling["factor"]  # `8` in the original implementation
+    low_freq_factor = config.rope_scaling["low_freq_factor"]  # `1` in the original implementation
+    high_freq_factor = config.rope_scaling["high_freq_factor"]  # `4` in the original implementation
+    old_context_len = config.rope_scaling["original_max_position_embeddings"]  # `8192` in the original implementation
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+
+    wavelen = 2 * math.pi / inv_freq
+    # wavelen < high_freq_wavelen: do nothing
+    # wavelen > low_freq_wavelen: divide by factor
+    inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
+    # otherwise: interpolate between the two, using a smooth factor
+    smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
+    is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
+    inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+
+    return inv_freq_llama, attention_factor
+
+
+# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
+# from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
+# parameterizations, as long as the callable has the same signature.
+ROPE_INIT_FUNCTIONS = {
+    "default": _compute_default_rope_parameters,
+    "linear": _compute_linear_scaling_rope_parameters,
+    "dynamic": _compute_dynamic_ntk_parameters,
+    "yarn": _compute_yarn_parameters,
+    "longrope": _compute_longrope_parameters,
+    "llama3": _compute_llama3_parameters,
+}
+
+
+def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
+    """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
+    # BC: "rope_type" was originally "type" -- let's gracefully handle it
+    if "rope_type" not in received_keys and "type" in received_keys:
+        received_keys -= {"type"}
+        received_keys.add("rope_type")
+
+    missing_keys = required_keys - received_keys
+    if missing_keys:
+        raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
+
+    if optional_keys is not None:
+        unused_keys = received_keys - required_keys - optional_keys
+    else:
+        unused_keys = received_keys - required_keys
+    if unused_keys:
+        logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
+
+
+def _validate_default_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+
+def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_yarn_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    attention_factor = rope_scaling.get("attention_factor")
+    if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
+        logger.warning(
+            f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+        )
+    beta_fast = rope_scaling.get("beta_fast")
+    if beta_fast is not None and not isinstance(beta_fast, float):
+        logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
+    beta_slow = rope_scaling.get("beta_slow")
+    if beta_slow is not None and not isinstance(beta_slow, float):
+        logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
+
+    if (beta_fast or 32) < (beta_slow or 1):
+        logger.warning(
+            f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
+            f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
+        )
+
+
+def _validate_longrope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "short_factor", "long_factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+
+    short_factor = rope_scaling.get("short_factor")
+    if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
+        logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
+    if not len(short_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
+
+    long_factor = rope_scaling.get("long_factor")
+    if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
+        logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
+    if not len(long_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
+
+    # Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
+    # `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
+    # unique to longrope (= undesirable)
+    if hasattr(config, "original_max_position_embeddings"):
+        logger.warning_once(
+            "This model has set a `original_max_position_embeddings` field, to be used together with "
+            "`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`"
+            "with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
+            "as it is compatible with most model architectures."
+        )
+    else:
+        factor = rope_scaling.get("factor")
+        if factor is None:
+            logger.warning("Missing required keys in `rope_scaling`: 'factor'")
+        elif not isinstance(factor, float) or factor < 1.0:
+            logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+        attention_factor = rope_scaling.get("attention_factor")
+        if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
+            logger.warning(
+                f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+            )
+
+
+def _validate_llama3_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    low_freq_factor = rope_scaling["low_freq_factor"]
+    high_freq_factor = rope_scaling["high_freq_factor"]
+    if low_freq_factor is None or not isinstance(low_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+    if high_freq_factor is None or not isinstance(high_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+    if high_freq_factor <= low_freq_factor:
+        logger.warning(
+            "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
+            f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
+        )
+
+    original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
+    if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
+            f"{original_max_position_embeddings}"
+        )
+    if original_max_position_embeddings >= config.max_position_embeddings:
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
+            f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
+        )
+
+
+# Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
+ROPE_VALIDATION_FUNCTIONS = {
+    "default": _validate_default_rope_parameters,
+    "linear": _validate_linear_scaling_rope_parameters,
+    "dynamic": _validate_dynamic_scaling_rope_parameters,
+    "yarn": _validate_yarn_parameters,
+    "longrope": _validate_longrope_parameters,
+    "llama3": _validate_llama3_parameters,
+}
+
+
+def rope_config_validation(config: PretrainedConfig):
+    """
+    Validate the RoPE config arguments, given a `PretrainedConfig` object
+    """
+    rope_scaling = getattr(config, "rope_scaling", None)  # not a default parameter in `PretrainedConfig`
+    if rope_scaling is None:
+        return
+
+    # BC: "rope_type" was originally "type"
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+    validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
+    if validation_fn is not None:
+        validation_fn(config)
+    else:
+        logger.warning(
+            f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
+        )