Text Generation
Transformers
Safetensors
lafa
feature-extraction
qwen3
flow-matching
speculative-decoding
custom_code
conversational
Instructions to use lafa-hf/lafa-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lafa-hf/lafa-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lafa-hf/lafa-8B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lafa-hf/lafa-8B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lafa-hf/lafa-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lafa-hf/lafa-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lafa-hf/lafa-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lafa-hf/lafa-8B
- SGLang
How to use lafa-hf/lafa-8B 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 "lafa-hf/lafa-8B" \ --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": "lafa-hf/lafa-8B", "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 "lafa-hf/lafa-8B" \ --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": "lafa-hf/lafa-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use lafa-hf/lafa-8B with Docker Model Runner:
docker model run hf.co/lafa-hf/lafa-8B
Commit History
12 days ago
-
Group results and fold output decoding into usage
Verified signaturedef04f3 -
Use paper terminology for decoding sections
Verified signatureee97693 -
Use the model name as the card title
Verified signature27f8e3f -
Revise model card introduction
Verified signature90dd038 -
Add arXiv paper link and citation
Verified signature4216688 -
Update model card
Verified signature1ea4dd1
13 days ago
-
Sync model code with the release repository
Verified signatured50c455
28 days ago
-
Add BF16 EMA weights as the default model
Verified signaturee64f6d5
30 days ago
-
Add root AutoClass modules for subfolder loading
Verified signatureb4477c1 -
Add original training weights in raw subfolder
Verified signaturee683b44
-
initial commit
Verified signaturefefafca