Text Generation
Transformers
Safetensors
mistral
function-call
conversational
text-generation-inference
Instructions to use InterSync/Mistral-7B-Instruct-v0.2-Function-Calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InterSync/Mistral-7B-Instruct-v0.2-Function-Calling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="InterSync/Mistral-7B-Instruct-v0.2-Function-Calling") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("InterSync/Mistral-7B-Instruct-v0.2-Function-Calling") model = AutoModelForCausalLM.from_pretrained("InterSync/Mistral-7B-Instruct-v0.2-Function-Calling") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use InterSync/Mistral-7B-Instruct-v0.2-Function-Calling with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InterSync/Mistral-7B-Instruct-v0.2-Function-Calling" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InterSync/Mistral-7B-Instruct-v0.2-Function-Calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/InterSync/Mistral-7B-Instruct-v0.2-Function-Calling
- SGLang
How to use InterSync/Mistral-7B-Instruct-v0.2-Function-Calling 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 "InterSync/Mistral-7B-Instruct-v0.2-Function-Calling" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InterSync/Mistral-7B-Instruct-v0.2-Function-Calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "InterSync/Mistral-7B-Instruct-v0.2-Function-Calling" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InterSync/Mistral-7B-Instruct-v0.2-Function-Calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use InterSync/Mistral-7B-Instruct-v0.2-Function-Calling with Docker Model Runner:
docker model run hf.co/InterSync/Mistral-7B-Instruct-v0.2-Function-Calling
Update special_tokens_map.json
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special_tokens_map.json
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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"single_word": false
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},
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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"pad_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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