Instructions to use Sandiago21/llama-7b-hf-prompt-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandiago21/llama-7b-hf-prompt-answering with Transformers:

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

pipe = pipeline("text-generation", model="Sandiago21/llama-7b-hf-prompt-answering")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering")
model = AutoModelForCausalLM.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering", device_map="auto")

PEFT
How to use Sandiago21/llama-7b-hf-prompt-answering with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sandiago21/llama-7b-hf-prompt-answering with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandiago21/llama-7b-hf-prompt-answering"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandiago21/llama-7b-hf-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering

SGLang

How to use Sandiago21/llama-7b-hf-prompt-answering 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 "Sandiago21/llama-7b-hf-prompt-answering" \
    --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": "Sandiago21/llama-7b-hf-prompt-answering",
		"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 "Sandiago21/llama-7b-hf-prompt-answering" \
        --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": "Sandiago21/llama-7b-hf-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sandiago21/llama-7b-hf-prompt-answering with Docker Model Runner:
```
docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering
```

llama-7b-hf-prompt-answering / pytorch_model-00032-of-00039.bin

Sandiago21

commit my first llama-7b-hf model

7ec7d8a about 3 years ago

Download

History Blame

371 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: eddf35cc33b5fd6b5e87ee736ca29176fbbc5406f986b3922bb31bfb33efb7e5
Size of remote file:: 371 MB
SHA256:: 1da7fdd75386dcb49549fe47512cfe121bc1e1b35288009d02ab86563810d48d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.