Text Generation
Transformers
Safetensors
mistral
openchat
C-RLFT
conversational
text-generation-inference
Instructions to use LucciAI/openchat-3.5-0106-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LucciAI/openchat-3.5-0106-function-calling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LucciAI/openchat-3.5-0106-function-calling") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LucciAI/openchat-3.5-0106-function-calling") model = AutoModelForCausalLM.from_pretrained("LucciAI/openchat-3.5-0106-function-calling", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LucciAI/openchat-3.5-0106-function-calling with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LucciAI/openchat-3.5-0106-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": "LucciAI/openchat-3.5-0106-function-calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LucciAI/openchat-3.5-0106-function-calling
- SGLang
How to use LucciAI/openchat-3.5-0106-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 "LucciAI/openchat-3.5-0106-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": "LucciAI/openchat-3.5-0106-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 "LucciAI/openchat-3.5-0106-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": "LucciAI/openchat-3.5-0106-function-calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use LucciAI/openchat-3.5-0106-function-calling with Docker Model Runner:
docker model run hf.co/LucciAI/openchat-3.5-0106-function-calling
Download tokenizer_config.json from LucciAI/openchat-3.5-0106-function-calling: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/LucciAI/openchat-3.5-0106-function-calling/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://LucciAI/openchat-3.5-0106-function-calling/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/LucciAI/openchat-3.5-0106-function-calling/resolve/main/tokenizer_config.json
2.1 kB
| { | |
| "add_bos_token": true, | |
| "add_eos_token": false, | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "<s>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "</s>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "32000": { | |
| "content": "<|end_of_turn|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "32001": { | |
| "content": "<|pad_0|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "additional_special_tokens": [ | |
| "<|end_of_turn|>", | |
| "<|pad_0|>" | |
| ], | |
| "bos_token": "<s>", | |
| "chat_template": "{{ bos_token }} [INST] {% for message in messages %}{% if message['role'] == 'system' %}<<SYS>>\n{{ message['content'] }}\n<</SYS>>\n\n{% elif message['role'] == 'function_metadata' %}You have access to the following functions. Use them if required:\n\n{{ message['content'] }}\n\n{% elif message['role'] == 'user' %}{{ message['content'] }} [/INST]\n\n{% elif message['role'] == 'assistant' %}{{ message['content'] }} [INST] {% elif message['role'] == 'function_call' %}{{ message['content'] }} [INST] {% elif message['role'] == 'function_response' %}Here is the response to the function call. If helpful, use it to respond to my question:\n\n{{ message['content'] }} [/INST]\n\n{% endif %}{% endfor %}", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|end_of_turn|>", | |
| "legacy": true, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<unk>", | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "LlamaTokenizer", | |
| "unk_token": "<unk>", | |
| "use_default_system_prompt": true | |
| } | |