Text Generation
Transformers
Safetensors
mistral
openchat
C-RLFT
conversational
text-generation-inference
Instructions to use openchat/openchat-3.5-0106 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openchat/openchat-3.5-0106 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openchat/openchat-3.5-0106") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106") model = AutoModelForCausalLM.from_pretrained("openchat/openchat-3.5-0106") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openchat/openchat-3.5-0106 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openchat/openchat-3.5-0106" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openchat/openchat-3.5-0106", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/openchat/openchat-3.5-0106
- SGLang
How to use openchat/openchat-3.5-0106 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 "openchat/openchat-3.5-0106" \ --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": "openchat/openchat-3.5-0106", "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 "openchat/openchat-3.5-0106" \ --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": "openchat/openchat-3.5-0106", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use openchat/openchat-3.5-0106 with Docker Model Runner:
docker model run hf.co/openchat/openchat-3.5-0106
[AUTOMATED] Model Memory Requirements
#14 opened almost 2 years ago
by
model-sizer-bot
Issue with Unexpected Tokens and Long Responses in Model Outputs
#13 opened about 2 years ago
by
UlanYisaev
Train OpenChat on Mistral-10.7B-v0.2
#12 opened about 2 years ago
by
Joseph717171
Need Help in invoking endpoint from sagemaker.
#11 opened about 2 years ago
by
Faiz4work
Loading checkpoint shards error
#10 opened about 2 years ago
by
WindowsArinkin
Instruct-finetuning dataset
1
#9 opened about 2 years ago
by
Andriy
32k context length
2
#7 opened about 2 years ago
by
alpayariyak
Adding Evaluation Results
#6 opened about 2 years ago
by
leaderboard-pr-bot
Update README.md
#5 opened about 2 years ago
by
typowy-93
Update examples in README to be compatible with soon-to-come ChatWidget
#4 opened about 2 years ago
by
Wauplin
Performance for structured responses
#3 opened over 2 years ago
by
RonanMcGovern
Train Mistral 7B 0.2
👍 8
9
#2 opened over 2 years ago
by
mosama