Instructions to use yeen214/llama2_7b_platypus_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yeen214/llama2_7b_platypus_tuning with Transformers:

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

pipe = pipeline("text-generation", model="yeen214/llama2_7b_platypus_tuning")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yeen214/llama2_7b_platypus_tuning")
model = AutoModelForCausalLM.from_pretrained("yeen214/llama2_7b_platypus_tuning")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yeen214/llama2_7b_platypus_tuning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yeen214/llama2_7b_platypus_tuning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yeen214/llama2_7b_platypus_tuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yeen214/llama2_7b_platypus_tuning

SGLang

How to use yeen214/llama2_7b_platypus_tuning 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 "yeen214/llama2_7b_platypus_tuning" \
    --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": "yeen214/llama2_7b_platypus_tuning",
		"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 "yeen214/llama2_7b_platypus_tuning" \
        --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": "yeen214/llama2_7b_platypus_tuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yeen214/llama2_7b_platypus_tuning with Docker Model Runner:
```
docker model run hf.co/yeen214/llama2_7b_platypus_tuning
```

finetuning model(instruction tuning) with garage-bAInd/Open-Platypus

base model : https://huggingface.co/meta-llama/Llama-2-7b

About llama2

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B finetuned model with Open-Platypus dataset.

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yeen214
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llama2_7b_platypus_tuning

Dataset used to train yeen214/llama2_7b_platypus_tuning