Text Generation
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
custom_code
text-generation-inference
Instructions to use flytech/togetherchat-dev-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flytech/togetherchat-dev-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flytech/togetherchat-dev-7b", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("flytech/togetherchat-dev-7b", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("flytech/togetherchat-dev-7b", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use flytech/togetherchat-dev-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flytech/togetherchat-dev-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flytech/togetherchat-dev-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/flytech/togetherchat-dev-7b
- SGLang
How to use flytech/togetherchat-dev-7b 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 "flytech/togetherchat-dev-7b" \ --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": "flytech/togetherchat-dev-7b", "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 "flytech/togetherchat-dev-7b" \ --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": "flytech/togetherchat-dev-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use flytech/togetherchat-dev-7b with Docker Model Runner:
docker model run hf.co/flytech/togetherchat-dev-7b
- Xet hash:
- a0b655387807a2df859a59eb2a1f79768e60dea03811f7e4256ad58111123b3f
- Size of remote file:
- 4.33 GB
- SHA256:
- cd390fd9622d0b3b6ce420e5e0735b8c5d00638df2f4e14d9b47ee193d6b4a7e
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