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 "appvoid/palmer-002" \
--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": "appvoid/palmer-002",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'
palmer
a better base model
palmer is a series of ~1b parameters language models fine-tuned to be used as base models instead of using custom prompts for tasks. This means that it can be further fine-tuned on more data with custom prompts as usual or be used for downstream tasks as any base model you can get. The model has the best of both worlds: some "bias" to act as an assistant, but also the abillity to predict the next-word from its internet knowledge base. It's a 1.1b llama 2 model so you can use it with your favorite tools/frameworks.
evaluation 🧪
note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals
Model ARC_C HellaSwag PIQA Winogrande Average
tinyllama-2 | 0.2807 | 0.5463 | 0.7067 | 0.5683 | 0.5255 |
palmer-001 | 0.2807 | 0.5524 | 0.7106 | 0.5896 | 0.5333 |
babbage-001 | 0.2944 | 0.5448 | 0.7410 | 0.5935 | 0.5434 |
deacon-1b | 0.2944 | 0.5727 | 0.7040 | 0.5801 | 0.5434 |
tinyllama-2.5 | 0.3191 | 0.5896 | 0.7307 | 0.5872 | 0.5566 |
palmer-002 | 0.3242 | 0.5956 | 0.7345 | 0.5888 | 0.5607 |
babbage-002 | 0.3285 | 0.6380 | 0.7606 | 0.6085 | 0.5839 |
This model shows exceptional performance and as of now is the best tinyllama-size base model. Furthermore, this proves LIMA paper point and serves as a good open-source alternative to openai's babbage-002.
training 🦾
Training took ~3.5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. palmer was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.
prompt 📝
no prompt 🚀
Choose this if you prefer a base model without too much fine-tuning.
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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 "appvoid/palmer-002" \ --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": "appvoid/palmer-002", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'