Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
llama
Generated from Trainer
text-generation-inference
Instructions to use flytech/devchat-llama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flytech/devchat-llama-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flytech/devchat-llama-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("flytech/devchat-llama-7b") model = AutoModelForCausalLM.from_pretrained("flytech/devchat-llama-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use flytech/devchat-llama-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flytech/devchat-llama-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/devchat-llama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/flytech/devchat-llama-7b
- SGLang
How to use flytech/devchat-llama-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/devchat-llama-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/devchat-llama-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/devchat-llama-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/devchat-llama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use flytech/devchat-llama-7b with Docker Model Runner:
docker model run hf.co/flytech/devchat-llama-7b
Ctrl+K
- last-checkpoint
- runs
- 1.52 kB
- 1.2 kB
- 530 Bytes
- 16.9 MB xet
- 40 MB xet
- 659 Bytes
- 88 Bytes xet
- 102 Bytes xet
- 88 Bytes xet
- 88 Bytes xet
- 3.22 kB xet
- 437 Bytes xet
- 4.88 kB xet
- 88 Bytes xet
- 4.51 kB xet
- 88 Bytes xet
- 4.51 kB xet
- 88 Bytes xet
- 4.51 kB xet
- 88 Bytes xet
- 88 Bytes xet
- 4.51 kB xet
- 88 Bytes xet
- 4.18 kB xet
- 437 Bytes xet
- 4.88 kB xet
- 88 Bytes xet
- 224 Bytes xet
- 4.65 kB xet
- 4.87 kB xet
- 4.87 kB xet
- 4.49 kB xet
- 4.49 kB xet
- 4.49 kB xet
- 5.24 kB xet
- 132 Bytes
- 1.07 GB xet
- 3.5 GB xet
- 24 kB
- 667 MB xet
- 434 Bytes
- 512 kB xet
- 797 Bytes
- 4.03 kB xet