Instructions to use PrincetonPLI/Llama-3-8B-Instruct-SkillMix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PrincetonPLI/Llama-3-8B-Instruct-SkillMix with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PrincetonPLI/Llama-3-8B-Instruct-SkillMix")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PrincetonPLI/Llama-3-8B-Instruct-SkillMix")
model = AutoModelForCausalLM.from_pretrained("PrincetonPLI/Llama-3-8B-Instruct-SkillMix")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PrincetonPLI/Llama-3-8B-Instruct-SkillMix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PrincetonPLI/Llama-3-8B-Instruct-SkillMix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrincetonPLI/Llama-3-8B-Instruct-SkillMix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PrincetonPLI/Llama-3-8B-Instruct-SkillMix

SGLang

How to use PrincetonPLI/Llama-3-8B-Instruct-SkillMix with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PrincetonPLI/Llama-3-8B-Instruct-SkillMix" \
    --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": "PrincetonPLI/Llama-3-8B-Instruct-SkillMix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "PrincetonPLI/Llama-3-8B-Instruct-SkillMix" \
        --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": "PrincetonPLI/Llama-3-8B-Instruct-SkillMix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PrincetonPLI/Llama-3-8B-Instruct-SkillMix with Docker Model Runner:
```
docker model run hf.co/PrincetonPLI/Llama-3-8B-Instruct-SkillMix
```

Model Card for Llama-3-8B-Instruct-SkillMix

This model was SFT-ed from meta-llama/Meta-Llama-3-8B with data generated by the Seed-Dataset Agnostic version of the Instruct-SkillMix pipeline.

Training Details

We used 4000 examples from Instruct-SkillMix-SDA(k=2) (data available at PrincetonPLI/Instruct-SkillMix-SDA).

LR: 2e-5
- Linear Warmup Ratio: 0.03
- Decay: Cosine Decay to 0
Batch Size: 128
epoch: 7 / 15
Optimizer: AdamW
Sequence Length: 1024

Evaluation Details

We provide the set of generation configuration used for evaluation.

AlpacaEval

model_kwargs:
- torch_dtype: 'bfloat16'
- max_new_tokens: 2048
temperature: 0.9
top_p: 1.0
do_sample: True
stop_token_ids:
- 128001
- 128009

MTBench

model_kwargs:
- torch_dtype: 'bfloat16'
- max_new_tokens: 1024
temperature: 0.7
stop_token_ids:
- 128001
- 128009

WildBench

model_kwargs:
- torch_dtype: 'bfloat16'
- max_new_tokens: 4096
temperature: 0.9
top_p: 1.0
do_sample: True
stop_token_ids:
- 128001
- 128009

Citation

Paper: Instruct-SkillMix

@misc{kaur2024instructskillmixpowerfulpipelinellm,
      title={Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning}, 
      author={Simran Kaur and Simon Park and Anirudh Goyal and Sanjeev Arora},
      year={2024},
      eprint={2408.14774},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2408.14774}, 
}