Instructions to use hbx/Mistral-Interact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hbx/Mistral-Interact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hbx/Mistral-Interact")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hbx/Mistral-Interact") model = AutoModelForCausalLM.from_pretrained("hbx/Mistral-Interact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hbx/Mistral-Interact with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hbx/Mistral-Interact" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hbx/Mistral-Interact", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hbx/Mistral-Interact
- SGLang
How to use hbx/Mistral-Interact 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 "hbx/Mistral-Interact" \ --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": "hbx/Mistral-Interact", "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 "hbx/Mistral-Interact" \ --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": "hbx/Mistral-Interact", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hbx/Mistral-Interact with Docker Model Runner:
docker model run hf.co/hbx/Mistral-Interact
Update README.md
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README.md
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- **Enhanced model-user interaction experience:** Mistral-Interact inquires about missing details in vague tasks more reasonably and friendly than other open-source models, thus promoting a clearer understanding of the user’s implicit intentions.
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- **Comparable performance with closed-source GPT-4:** We prove that smaller-scale model experts can approach or even exceed general-purpose large-scale models across various aspects including vagueness judgment, comprehensiveness of summaries, and friendliness of interaction.
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We utilize the [model-center](https://github.com/OpenBMB/ModelCenter) framework to conduct full-parameter fine-tuning of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using [Intention-in-Interaction(IN3)](https://huggingface.co/datasets/hbx/IN3) dataset on two 80GB A800s. For full details and the usage of this model please read our [paper](https://arxiv.org/abs/2402.09205) and [repo](https://github.com/HBX-hbx/Mistral-Interact).
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# Citation
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- **Enhanced model-user interaction experience:** Mistral-Interact inquires about missing details in vague tasks more reasonably and friendly than other open-source models, thus promoting a clearer understanding of the user’s implicit intentions.
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- **Comparable performance with closed-source GPT-4:** We prove that smaller-scale model experts can approach or even exceed general-purpose large-scale models across various aspects including vagueness judgment, comprehensiveness of summaries, and friendliness of interaction.
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We utilize the [model-center](https://github.com/OpenBMB/ModelCenter) framework to conduct full-parameter fine-tuning of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using [Intention-in-Interaction(IN3)](https://huggingface.co/datasets/hbx/IN3) dataset on two 80GB A800s. For full details and the usage of this model please read our **[paper](https://arxiv.org/abs/2402.09205)** and **[repo](https://github.com/HBX-hbx/Mistral-Interact)**.
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# Citation
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