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 "Rexopia/HawkLM-demo" \
--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": "Rexopia/HawkLM-demo",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
HawkLM-demo
HawkLM-demo 🤗 ｜ HawkLM-Chat-demo 🤗
Model Details
- Developed by: Rexopia
- Reach me: ruiji.zhang@outlook.com
- Language(s): English
- License: Apache license 2.0
- Pretrained model: True
- Demo version: True
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Rexopia/HawkLM-demo", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Rexopia/HawkLM-demo", device_map="auto", trust_remote_code=True)
Training Data
We sampled English-only corpus from Redpajama-1T datasets without any Arxiv and GitHub tags. As the demo version presented, we only trained 3.3Bil tokens.
Evaluation
[More Information Needed]
Citation
[More Information Needed]
Model Card Contact
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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 "Rexopia/HawkLM-demo" \ --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": "Rexopia/HawkLM-demo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'