Create README.md

README.md

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+---
+library_name: transformers
+tags:
+- falcon-h1
+license: other
+license_name: falcon-llm-license
+license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
+---
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Training Details](#training-details)
+3. [Usage](#usage)
+4. [Evaluation](#evaluation)
+5. [Citation](#citation)
+
+# TL;DR
+
+# Model Details
+
+## Model Description
+
+- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
+- **Model type:** Causal decoder-only
+- **Architecture:** Hybrid Transformers + Mamba architecture
+- **Language(s) (NLP):** English, Multilingual
+- **License:** Falcon-LLM License
+
+# Training details
+
+For more details about the training protocol of this model, please refer to the [Falcon-H1 technical blogpost](https://falcon-lm.github.io/blog/falcon-h1/).
+
+# Usage
+
+Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or our custom fork of `llama.cpp` library.
+
+## Inference
+
+Make sure to install the latest version of `transformers` or `vllm`, eventually install these packages from source:
+
+```bash
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+Refer to [the official vLLM documentation for more details on building vLLM from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source).
+
+### 🤗 transformers
+
+Refer to the snippet below to run H1 models using 🤗 transformers:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "tiiuae/Falcon-H1-1B-Base"
+
+model = AutoModelForCausalLM.from_pretrained(
+  model_id,
+  torch_dtype=torch.bfloat16,
+  device_map="auto"
+)
+
+# Perform text generation
+```
+
+### vLLM
+
+For vLLM, simply start a server by executing the command below:
+
+```
+# pip install vllm
+vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1
+```
+
+### `llama.cpp`
+
+While we are working on integrating our architecture directly into `llama.cpp` library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 
+Use the same installing guidelines as `llama.cpp`.
+
+# Evaluation
+
+Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks. 
+
+| Tasks | Falcon-H1-1.5B | Qwen3-1.7B | Qwen2.5-1.5B | Gemma3-1B | Llama3.2-1B | Falcon3-1B |
+| --- | --- | --- | --- | --- | --- | --- |
+| **General**  | | | | | |
+| BBH | **46.57** | 43.05 | 40.55 | 30.26 | 30.72 | 35.24 |
+| MMLU | 61.81 | **62.46** | 61.13 | 26.33 | 32.39 | 45.14 |
+| ARC-C | 53.24 | **55.72** | 54.27 | 39.33 | 39.42 | 47.87 |
+| HellaSwag | 66.76 | 67.09 | **67.86** | 62.94 | 65.73 | 62.3 |
+| Winogrande | 65.59 | **66.3** | 64.56 | 62.59 | 62.75 | 61.17 |
+| **Math**  | | | | | |
+| GSM8k | 52.01 | **70.74** | 63.0 | 2.2 | 7.05 | 34.95 |
+| MATH lvl5 | **20.39** | 16.39 | 8.84 | 1.21 | 0.98 | 3.4 |
+| **Science**  | | | | | |
+| GPQA | 29.11 | **29.45** | 28.36 | 24.66 | 23.57 | 27.85 |
+| MMLU-Pro | **35.53** | 33.81 | 28.72 | 11.31 | 11.8 | 16.11 |
+| MMLU-stem | **63.37** | 61.53 | 54.93 | 27.59 | 30.19 | 40.06 |
+| **Code**  | | | | | |
+| HumanEval | 50.0 | **67.68** | 35.37 | 6.71 | 18.9 | 10.37 |
+| HumanEval+ | 42.68 | **60.98** | 29.27 | 5.49 | 16.46 | 9.15 |
+| MBPP | 65.08 | **67.72** | 60.05 | 12.7 | 35.98 | 12.43 |
+| MBPP+ | 55.03 | **58.99** | 49.47 | 9.52 | 29.89 | 9.52 |
+
+You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks.
+
+# Useful links
+
+- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/).
+- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
+
+# Citation
+
+If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite.
+
+```
+@misc{tiifalconh1,
+    title = {Falcon-H1},
+    author = {Falcon-LLM Team},
+    month = {May},
+    url = {https://falcon-lm.github.io/blog/falcon-h1},
+    year = {2025}
+}
+```