Instructions to use zyh3826/llama2-13b-ft-openllm-leaderboard-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zyh3826/llama2-13b-ft-openllm-leaderboard-v1 with Transformers:

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

pipe = pipeline("text-generation", model="zyh3826/llama2-13b-ft-openllm-leaderboard-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zyh3826/llama2-13b-ft-openllm-leaderboard-v1")
model = AutoModelForCausalLM.from_pretrained("zyh3826/llama2-13b-ft-openllm-leaderboard-v1")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zyh3826/llama2-13b-ft-openllm-leaderboard-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zyh3826/llama2-13b-ft-openllm-leaderboard-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zyh3826/llama2-13b-ft-openllm-leaderboard-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zyh3826/llama2-13b-ft-openllm-leaderboard-v1

SGLang

How to use zyh3826/llama2-13b-ft-openllm-leaderboard-v1 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 "zyh3826/llama2-13b-ft-openllm-leaderboard-v1" \
    --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": "zyh3826/llama2-13b-ft-openllm-leaderboard-v1",
		"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 "zyh3826/llama2-13b-ft-openllm-leaderboard-v1" \
        --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": "zyh3826/llama2-13b-ft-openllm-leaderboard-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zyh3826/llama2-13b-ft-openllm-leaderboard-v1 with Docker Model Runner:
```
docker model run hf.co/zyh3826/llama2-13b-ft-openllm-leaderboard-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

finetue LLAMA2-13B myself 20231024-172733

Model Details

Developed by: zyh3826
Backbone Model: llama-2-13B
Library: HuggingFace Transformers

Limitations & Biases:

Llama2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/

License Disclaimer:

This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.

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Safetensors

Model size

13B params

Tensor type

BF16

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Quantizations

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