How to use from
SGLangUse 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 "kdf/jiang-chat" \
--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": "kdf/jiang-chat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
- Developed by: 知未智能KDF
- Model type: JIANG
- Language(s) (NLP): Chinese
- License: Reasearch only (because using other Reasearch-Only SFT datasets)
- Finetuned from model [optional]: https://huggingface.co/kdf/jiang-base
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'kdf/jiang-chat'
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
ret = model.generate(**tokenizer('<|unused52|>写首诗<|unused53|><|unused54|>', return_tensors='pt'), max_new_tokens=50, top_k=1)
print(tokenizer.decode(ret[0]))
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "kdf/jiang-chat" \ --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": "kdf/jiang-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'