Instructions to use purbeshmitra/MOTIF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use purbeshmitra/MOTIF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="purbeshmitra/MOTIF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("purbeshmitra/MOTIF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use purbeshmitra/MOTIF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "purbeshmitra/MOTIF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "purbeshmitra/MOTIF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/purbeshmitra/MOTIF
- SGLang
How to use purbeshmitra/MOTIF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "purbeshmitra/MOTIF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "purbeshmitra/MOTIF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "purbeshmitra/MOTIF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "purbeshmitra/MOTIF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use purbeshmitra/MOTIF with Docker Model Runner:
docker model run hf.co/purbeshmitra/MOTIF
Add pipeline tag to model card
#1
by nielsr HF Staff - opened
README.md
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---
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base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
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library_name: peft
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license: apache-2.0
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datasets:
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- openai/gsm8k
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- HuggingFaceH4/MATH-500
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- HuggingFaceH4/aime_2024
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language:
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- en
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metrics:
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- accuracy
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---
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## MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
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SYSTEM_PROMPT = "You are a helpful assistant. When the user asks a question, you solve it in 3 rounds. In each round, you first think about the reasoning process of answering and then provide the user with a detailed progress about it. The reasoning process and the progress are enclosed within <reasoning> </reasoning> and <answer> </answer> tags, respectively. Therefore, you follow the strict format:
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<reasoning> reasoning process here </reasoning> <answer> detailed progress here </answer>
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The User provides this detailed progress as additional context in the next round. You then respond again with further thinking and further progress. When the User says that the current round is the final (third) round, you provide an answer inside the answer tags. You also enclose a final answer in third round in the box: \boxed{}. Only this boxed final answer is used for evaluation."
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```
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## Citation
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---
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base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
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datasets:
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- openai/gsm8k
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- HuggingFaceH4/MATH-500
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- HuggingFaceH4/aime_2024
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language:
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- en
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library_name: peft
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license: apache-2.0
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metrics:
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- accuracy
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pipeline_tag: text-generation
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---
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## MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
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SYSTEM_PROMPT = "You are a helpful assistant. When the user asks a question, you solve it in 3 rounds. In each round, you first think about the reasoning process of answering and then provide the user with a detailed progress about it. The reasoning process and the progress are enclosed within <reasoning> </reasoning> and <answer> </answer> tags, respectively. Therefore, you follow the strict format:
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<reasoning> reasoning process here </reasoning> <answer> detailed progress here </answer>
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+
The User provides this detailed progress as additional context in the next round. You then respond again with further thinking and further progress. When the User says that the current round is the final (third) round, you provide an answer inside the answer tags. You also enclose a final answer in third round in the box: \\boxed{}. Only this boxed final answer is used for evaluation."
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```
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## Citation
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