Instructions to use A2H0H0R1/Llama-2-7b-chat-hf-biology with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use A2H0H0R1/Llama-2-7b-chat-hf-biology with Transformers:

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

pipe = pipeline("text-generation", model="A2H0H0R1/Llama-2-7b-chat-hf-biology")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("A2H0H0R1/Llama-2-7b-chat-hf-biology")
model = AutoModelForCausalLM.from_pretrained("A2H0H0R1/Llama-2-7b-chat-hf-biology", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use A2H0H0R1/Llama-2-7b-chat-hf-biology with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "A2H0H0R1/Llama-2-7b-chat-hf-biology"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "A2H0H0R1/Llama-2-7b-chat-hf-biology",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/A2H0H0R1/Llama-2-7b-chat-hf-biology

SGLang

How to use A2H0H0R1/Llama-2-7b-chat-hf-biology 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 "A2H0H0R1/Llama-2-7b-chat-hf-biology" \
    --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": "A2H0H0R1/Llama-2-7b-chat-hf-biology",
		"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 "A2H0H0R1/Llama-2-7b-chat-hf-biology" \
        --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": "A2H0H0R1/Llama-2-7b-chat-hf-biology",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use A2H0H0R1/Llama-2-7b-chat-hf-biology with Docker Model Runner:
```
docker model run hf.co/A2H0H0R1/Llama-2-7b-chat-hf-biology
```

2023-11-29-06-20-56

This model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf on the A2H0H0R1/Animal-nutrition dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training results

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.7
Tokenizers 0.14.1

Downloads last month: 14

Model tree for A2H0H0R1/Llama-2-7b-chat-hf-biology

Base model

NousResearch/Llama-2-7b-chat-hf

Finetuned

(142)

this model

A2H0H0R1
/

Llama-2-7b-chat-hf-biology

2023-11-29-06-20-56

Model description

Intended uses & limitations

Training and evaluation data

Training results

Framework versions

Model tree for A2H0H0R1/Llama-2-7b-chat-hf-biology

Dataset used to train A2H0H0R1/Llama-2-7b-chat-hf-biology