Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
How to use nlpguy/StableProse with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="nlpguy/StableProse") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("nlpguy/StableProse")
model = AutoModelForCausalLM.from_pretrained("nlpguy/StableProse")How to use nlpguy/StableProse with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpguy/StableProse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "nlpguy/StableProse",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/nlpguy/StableProse
How to use nlpguy/StableProse with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "nlpguy/StableProse" \
--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": "nlpguy/StableProse",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "nlpguy/StableProse" \
--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": "nlpguy/StableProse",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use nlpguy/StableProse with Docker Model Runner:
docker model run hf.co/nlpguy/StableProse
EDIT: This Merge inherits bad IFEval Results from the Base Model, keep this in mind when using this model.
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using mistralai/Mistral-Nemo-Base-2407 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: mistralai/Mistral-Nemo-Base-2407
dtype: bfloat16
merge_method: model_stock
slices:
- sources:
- layer_range: [0, 40]
model: mistralai/Mistral-Nemo-Base-2407
- layer_range: [0, 40]
model: Undi95/LocalC-12B-e2.0
- layer_range: [0, 40]
model: shuttleai/shuttle-2.5-mini
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 16.32 |
| IFEval (0-Shot) | 19.72 |
| BBH (3-Shot) | 30.18 |
| MATH Lvl 5 (4-Shot) | 4.68 |
| GPQA (0-shot) | 7.05 |
| MuSR (0-shot) | 8.87 |
| MMLU-PRO (5-shot) | 27.43 |