Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 14
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 "TitleOS/ExperimentOne" \
--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": "TitleOS/ExperimentOne",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
ExperimentOne (Mistral-Hermes-Dolphin-7b)
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: mistralai/Mistral-7B-v0.1
- model: NousResearch/Hermes-2-Pro-Mistral-7B
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
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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 "TitleOS/ExperimentOne" \ --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": "TitleOS/ExperimentOne", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'