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 "not-lain/PyGPT" \
--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": "not-lain/PyGPT",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Model Details
this is the finetuned version of GPT2 on a coding dataset
Model Description
- Model type: text-generation
- Finetuned from model GPT2
Model Sources
- Repository: https://huggingface.co/gpt2
Uses
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="not-lain/PyGPT")
prompt = """
Below is an instruction that describes a task. Write a response that
appropriately completes the request.
### Instruction:
Create a function to calculate the sum of a sequence of integers.
### Input:
[1, 2, 3, 4, 5]
### Output:
"""
pipe(prompt)
Bias, Risks, and Limitations
model may produce biased ,erroneous and output.
Recommendations
it is not advised to use this model as it is just a product of testing a finetuning script
Training Details
Training Data
[More Information Needed]
Evaluation
please refer to the tensorboard tab for full details
- Downloads last month
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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 "not-lain/PyGPT" \ --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": "not-lain/PyGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'