Instructions to use OhCherryFire/llama2-7b-prontoqa-policy-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OhCherryFire/llama2-7b-prontoqa-policy-hf with Transformers:

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

pipe = pipeline("text-generation", model="OhCherryFire/llama2-7b-prontoqa-policy-hf")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OhCherryFire/llama2-7b-prontoqa-policy-hf")
model = AutoModelForCausalLM.from_pretrained("OhCherryFire/llama2-7b-prontoqa-policy-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OhCherryFire/llama2-7b-prontoqa-policy-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OhCherryFire/llama2-7b-prontoqa-policy-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OhCherryFire/llama2-7b-prontoqa-policy-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OhCherryFire/llama2-7b-prontoqa-policy-hf

SGLang

How to use OhCherryFire/llama2-7b-prontoqa-policy-hf 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 "OhCherryFire/llama2-7b-prontoqa-policy-hf" \
    --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": "OhCherryFire/llama2-7b-prontoqa-policy-hf",
		"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 "OhCherryFire/llama2-7b-prontoqa-policy-hf" \
        --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": "OhCherryFire/llama2-7b-prontoqa-policy-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OhCherryFire/llama2-7b-prontoqa-policy-hf with Docker Model Runner:
```
docker model run hf.co/OhCherryFire/llama2-7b-prontoqa-policy-hf
```

The supervised finetuned model for ProntoQA in Alphazero-like tree-search can guide large language model decoding and training, ICML 2024

@article{feng2023alphazero,
  title={Alphazero-like tree-search can guide large language model decoding and training},
  author={Feng, Xidong and Wan, Ziyu and Wen, Muning and Wen, Ying and Zhang, Weinan and Wang, Jun},
  journal={arXiv preprint arXiv:2309.17179},
  year={2023}
}

Downloads last month: 9

Paper for OhCherryFire/llama2-7b-prontoqa-policy-hf

Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Paper • 2309.17179 • Published Sep 29, 2023 • 2