--- title: Falcon-H1R-7B canonical_url: "https://www.modelscope.cn/models/unsloth/Falcon-H1R-7B" md_url: "https://www.modelscope.cn/models/unsloth/Falcon-H1R-7B.md" repository: unsloth/Falcon-H1R-7B last_updated: 2026-01-06 license: other pipeline_tag: text-generation tasks: - text-generation model_type: - falcon_h1 architectures: - FalconH1ForCausalLM base_model: - tiiuae/Falcon-H1R-7B base_model_relation: finetune parameters: 7.6B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - pytorch language: - en inference_backends: - "deploy_task text/emb" - "vllm 0.9.2" downloads: 128 stars: 0 tags: - falcon-h1r - unsloth --- # Falcon-H1R-7B > Falcon-H1R-7B - unsloth 在 ModelScope 开源的模型。[!NOTE] Includes Unsloth chat template fixes! For llama.cpp, use --jinja unsloth/Falcon-H1R-7B 是 ModelScope 魔搭社区上的 7.6B 参数text-generation模型，采用 other 许可，基于 tiiuae/Falcon-H1R-7B 构建，可用 deploy_task text/emb、vllm 0.9.2 部署。 - **Repository**: unsloth/Falcon-H1R-7B - **License**: other - **Tasks**: text-generation - **Parameters**: 7.6B - **Base model**: tiiuae/Falcon-H1R-7B - **Inference backends**: deploy_task text/emb, vllm 0.9.2 - **Tags**: falcon-h1r, unsloth - **Downloads**: 128 - **Stars**: 0 - **Last updated**: 2026-01-06 Source: https://www.modelscope.cn/models/unsloth/Falcon-H1R-7B --- > [!NOTE] > Includes Unsloth **chat template fixes**!
For `llama.cpp`, use `--jinja` >

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

drawing # Falcon-H1R-7B This repository presents **Falcon-H1R-7B**, a reasoning-specialized model built on top of [Falcon-H1-7B-Base](https://huggingface.co/tiiuae/Falcon-H1-7B-Base) and trained via cold-start supervised fine-tuning with long reasoning traces and further enhanced by scaling RL with GRPO. The model demonstrates outstanding performance across various benchmark evaluations, including mathematics, programming, instruction following, and general logic. ## Model Description - **Developed by:** [Technology Innovation Institute](https://www.tii.ae) - **Model type:** Causal decoder-only - **Architecture:** Hybrid (Transformers + Mamba2) architecture - **Language(s):** English, Multilingual - **License:** [Falcon-LLM License](https://falconllm.tii.ae/falcon-terms-and-conditions.html) ## Training details For more details about the training protocol of this model, please refer to the [Falcon-H1R technical blogpost](https://falcon-lm.github.io/blog/falcon-h1r-7b) and [Technical Report](https://github.com/tiiuae/falcon-h1r/blob/main/tech_report.pdf). # Usage Currently to use this model, you can either rely on Hugging Face `transformers`, `vLLM` or `SGLang` library. ## Inference Make sure to install the latest version of `transformers` or `vLLM` or `SGLang`. ```bash pip install transformers pip install mamba-ssm[causal-conv1d] ``` For vLLM, make sure to install `vllm=0.11.0`: ```bash pip install "vllm>=0.11.0" ``` ## Sampling Parameters We recommend using a **temperature** of **0.6** and **top-p** as **0.95** with max new tokens up to 65536. For supported frameworks, you can adjust the repetition_penalty and presence_penalty parameters to reduce endless repetitions. For reasoning tasks with continuous batching and requiring higher max new tokens, we recommend to use TP=2. ## 🤗 Transformers Refer to the snippet below to run H1R models using 🤗 transformers. Model will generate think content wrapped in a `...` block, followed by the final response.
Click to expand ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "tiiuae/Falcon-H1R-7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="auto") messages = [ {"role": "user", "content": "What is the derivative of x^2?"}, ] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") outputs = model.generate( inputs.to(model.device), max_new_tokens=65536, temperature=0.6, top_p=0.95, do_sample=True, ) print(tokenizer.decode(outputs[0])) ```
## vLLM For vLLM, simply start a server by executing the command below:
Click to expand
```python vllm serve tiiuae/Falcon-H1R-7B \ --tensor-parallel-size 1 \ --data-parallel-size 1 \ --reasoning-parser deepseek_r1 ```
Additional flags: * You can reduce `--max-model-len` to preserve memory. Default value is `262144` which is quite large but not necessary for most scenarios. vLLM client execution code: ```python from openai import OpenAI import json client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY", ) completion = client.chat.completions.create( model="tiiuae/Falcon-H1R-7B", messages=[ {"role": "user", "content": "If the product of two numbers is 360 and their GCD is 6, what is their LCM?"}, ], temperature=0.6, top_p=0.95, max_tokens=65536 ) msg = completion.choices[0].message print(json.dumps({ "reasoning": msg.reasoning_content, "answer": msg.content }, indent=2)) ```
## SGLang For SGLang, simply start a server by executing the command below:
Click to expand
```python python -m sglang.launch_server \ --model tiiuae/Falcon-H1R-7B \ --tensor-parallel-size 1 \ --reasoning-parser deepseek-r1 ```
SGLang client execution code: ```python from openai import OpenAI import json client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY", ) completion = client.chat.completions.create( model="tiiuae/Falcon-H1R-7B", messages=[ {"role": "user", "content": "How many solutions in integers satisfy ∣x∣+∣y∣=20?"}, ], max_tokens=65536, temperature=0.6, top_p=0.95, ) msg = completion.choices[0].message print(json.dumps({ "reasoning": msg.reasoning_content, "answer": msg.content }, indent=2)) ```

# Evaluation Falcon-H1R achieves state of art results in reasoning benchmarks.
Category Benchmark Falcon-H1R-7B Qwen3-8B DeepSeek-R1-0528-Qwen3-8B Phi-4-Reasoning-Plus-14B Apriel-1.5-15b-Thinker GPT-OSS-20B Qwen3-32B Nemotron-H-47B-Reasoning
MATH AIME24 88.1 77.9 83.3 77.2 86.2 83.3 79.4 64.6
AIME25 83.1 65.8 75.8 71.2 80.0 84.4 71.0 51.4
HMMT25 64.9 41.0 54.3 47.7 61.0 64.8 49.8 34.2
AMO-BENCH 36.3 14.1 23.3 15.0 22.2 26.0 21.3 7.0
MATH500 97.4 97.4 96.8 95.4 97.2 94.8 96.8 91.4
Code LCBv5-v6 68.6 53.0 57.2 53.1 53.0 72.0 61.0 47.4
SciCode (sub/main) 28.3 / 3.9 28.3 / 6.7 22.2 / 2.6 29.8 / 7.2 31.9 / 8.2 34.9 / 6.2 36.4 / 9.2 26.1 / 4.6
General GPQA-D 61.3 61.2 61.4 67.9 68.2 61.2 67.3 56.8
MMLU-Pro 72.1 63.5 69.1 79.2 76.5 75.6 73.9 78.6
HLE 11.1 4.2 5.6 5.9 12.0 9.8 8.3 4.4
IFBench 53.4 35.3 29.2 51.7 55.8 69.4 35.4 34.3
Agentic Workflows 𝜏²-Bench Telecom 25.4 27.8 68.4 60.2 29.8 11.4
Terminal-Bench Hard 4.9 2.1 1.4 2.1 9.9 9.9 2.8 1.4
TTS represents test time scaling results on few of the benchmarks that we evaluated via [DeepConf](https://github.com/facebookresearch/deepconf). Note that AMO-Bench* is limited to the parser-verifiable subset which comprises 39 problems.
Benchmark Falcon-H1R-7B Qwen3-8B DeepSeek-R1-0528-Qwen3-8B Nemotron-H-8B Phi-4-Reasoning-Plus-14B Qwen3-32B
AIME24 96.7 80.0 90.0 53.3 86.7 86.7
AIME25 96.7 80.0 82.8 43.3 83.3 86.7
GPQA-D 70.2 60.9 59.9 61.1 73.2 70.1
AMO-Bench* 35.9 15.4 25.6 7.7 20.5 28.2
# Useful links - View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1r-7b). - View [our technical report](https://arxiv.org/abs/2601.02346). - Feel free to join [our discord server](https://discord.gg/Cbek57PrZE) if you have any questions or to interact with our researchers and developers. # Citation If the Falcon-H1R family of reasoning models is helpful to your work, feel free to give us a cite. ``` @misc{falcon-h1r, title={Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling}, author={Falcon LLM Team and Iheb Chaabane and Puneesh Khanna and Suhail Mohmad and Slim Frikha and Shi Hu and Abdalgader Abubaker and Reda Alami and Mikhail Lubinets and Mohamed El Amine Seddik and Hakim Hacid}, year={2026}, eprint={2601.02346}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2601.02346}, } ```