Instructions to use eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch with Transformers:

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

pipe = pipeline("text-generation", model="eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch")
model = AutoModelForMultimodalLM.from_pretrained("eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch

SGLang

How to use eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch 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 "eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch" \
    --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": "eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch",
		"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 "eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch" \
        --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": "eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch with Docker Model Runner:
```
docker model run hf.co/eduardo-alvarez/llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch
```

llama-3-8b-Ecommerce_FAQ-loRA-Merged-10epoch / model-00005-of-00007.safetensors

eduardo-alvarez

Upload LlamaForCausalLM

ca110d2 verified almost 2 years ago

Download

History Blame Contribute Delete

4.83 GB

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:: 9b8b15c26736913ab5e3a5ed3060cd159624d05ecb07ad6cc2a7e0fddf131ff1
Size of remote file:: 4.83 GB
SHA256:: 4b9c49a7e5f6b66b508b884a4e091624f6b406a5a2d683397c4f76e4f2fa1f5a

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