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  library_name: transformers
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  tags:
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  - mamba
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- - falcon3
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  - reasoning
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  base_model:
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  - tiiuae/Falcon3-Mamba-7B-Instruct
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  pipeline_tag: text-generation
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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  ## Model Details
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- ### Model Description
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-
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- This model is a fine tuned version of Falcon3 Mamba 7 billion instruct.
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- It is tailored to reason and build logic before answering to the user question. The Mamba based model scales linearly with increased number of tokens, making it a very fast reasoner while maintaining consistent response quality.
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- This is from an earlier checkpoint of the model training.
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- - **Developed by:** Hanzla Javaid
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- - **Model type:** Mamba
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- ### Model Sources [optional]
 
 
 
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
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- [More Information Needed]
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- ### Downstream Use [optional]
 
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
 
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
 
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
 
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
 
 
 
 
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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- ### Training Procedure
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-
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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-
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- [More Information Needed]
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- #### Training Hyperparameters
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-
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
 
 
 
 
 
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  ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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-
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
 
 
 
 
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
 
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- ## Model Card Contact
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- [More Information Needed]
 
 
 
 
 
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  library_name: transformers
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  tags:
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  - mamba
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+ - deepseek
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  - reasoning
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  base_model:
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  - tiiuae/Falcon3-Mamba-7B-Instruct
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  pipeline_tag: text-generation
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  ---
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+ # Model Card: Falcon3-Mamba-R1-v0
 
 
 
 
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  ## Model Details
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+ **Model Description:**
 
 
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+ This model is a fine-tuned version of Falcon3-Mamba-7B-Instruct, optimized for logical reasoning and structured problem-solving before generating responses.
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+ It leverages the Mamba architecture, which scales linearly with an increased number of tokens, making it an efficient and fast reasoning model while maintaining high response quality.
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+ This fine-tuned version comes from an earlier checkpoint of the fine tuning pipeline.
 
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+ * **Developed by:** Hanzla Javaid
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+ * **Base Model:** tiiuae/Falcon3-Mamba-7B-Instruct
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+ * **Model Type:** Mamba-based causal decoder
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+ * **Model Release Date:** March 2025
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+ ## Intended Uses
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+ **Direct Use:**
 
 
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+ This model is designed for:
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+ * Reasoning-heavy tasks (math, logic, and structured problem-solving)
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+ * STEM-based question-answering
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+ * General-purpose text generation
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+ **Downstream Use:**
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+ * Fine-tuning for domain-specific applications such as finance, law, medicine, and research.
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+ * Integration into chatbots and virtual assistants that require advanced reasoning skills.
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+ * Enhancement of automated coding assistants with structured logic building.
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+ **Out-of-Scope Use:**
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+ * Misinformation or deceptive applications
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+ * Automated decision-making in high-risk fields (e.g., medical diagnosis without human oversight)
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+ * Bias-sensitive applications where fairness is critical but not explicitly controlled
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+ ## Bias and Limitations
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+ **Known Biases:**
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+ * The model prioritizes English language data, so performance on multilingual tasks may be weaker.
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+ * Fine-tuning may introduce or amplify biases present in the training data, especially in areas like ethics, politics, and cultural perspectives.
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+ **Technical Limitations:**
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+ * Performance may degrade on long-form generation beyond 64K tokens.
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+ **Recommendations:**
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+ * Users should verify outputs for accuracy, especially in critical applications.
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+ * Regular bias evaluation should be conducted when deploying in production environments.
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+ ## Getting Started
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+ To use this model, you can load it with transformers:
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+ ```python
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+ repo_name = "hanzla/Falcon3-Mamba-R1-v0"
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+ tokenizer = AutoTokenizer.from_pretrained(repo_name)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ repo_name,
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+ device_map="auto",
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+ torch_dtype=torch.float16,
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+ )
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+ def generate_text(prompt,generation_model,generation_tokenizer,max_tokens=1024):
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+ messages = [
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+ {"role": "system", "content": "You are a helpful assistant"},
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+ {"role": "user", "content": prompt},
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+ ]
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+ input_text = generation_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ print(input_text)
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+ input_ids = generation_tokenizer(input_text, return_tensors="pt").input_ids.to("auto")
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+ outputs = generation_model.generate(input_ids, max_new_tokens=max_tokens)
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+ generated_tokens = outputs[0][len(input_ids[0]):]
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+ return tokenizer.decode(generated_tokens, skip_special_tokens=True)
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+
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+ ```
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  ## Training Details
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+ **Training Procedure:**
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+ * **Pretrained Base Model:** Falcon3-Mamba-7B-Instruct
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+ * **Fine-tuning Data:** A subset of STEM problems from open-thoughts/OpenThoughts-114k
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+ * **Training Strategy:** GRPO
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+ * **Training Hyperparameters:**
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+ * **Batch Size:** 4
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+ * **Epochs:** 3
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+ * **Precision:** Mixed (fp16 / bf16)
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+ * **Hardware:** 2xH100 GPUs
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  ## Evaluation
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+ **Testing Data and Metrics:**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ The fine-tuned model's performance was evaluated on a variety of benchmarks to assess its reasoning abilities and overall capabilities. The table below presents a comparison between the fine-tuned model and the base model:
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+ | Category | Benchmark | Falcon3-Mamba-R1-v0 | Base Falcon3-Mamba-7B-Instruct |
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+ |---------------|--------------------------------|----------------------------------------|---------------------------------|
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+ | General | MMLU (5-shot) | 72.1 | 65.3 |
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+ | Math | GSM8K (5-shot) | 89.5 | 65.2 |
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+ | Reasoning | Arc Challenge (25-shot) | 75.8 | 53.7 |
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+ ## Technical Specifications
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+ **Model Architecture:**
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+ * **Mamba Blocks:** 64
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+ * **Hidden Size:** 4096
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+ **Software Requirements:**
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+ * `transformers >= 4.38`
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+ * `torch >= 2.1`
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+ * `accelerate >= 0.25`
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+ * `mamba-ssm`
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+ * `causal-conv1d>=1.4.0`