Instructions to use beratcmn/Llama3-ChatQA-1.5-8B-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beratcmn/Llama3-ChatQA-1.5-8B-lora with Transformers:

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

pipe = pipeline("text-generation", model="beratcmn/Llama3-ChatQA-1.5-8B-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("beratcmn/Llama3-ChatQA-1.5-8B-lora", device_map="auto")

PEFT
How to use beratcmn/Llama3-ChatQA-1.5-8B-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beratcmn/Llama3-ChatQA-1.5-8B-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beratcmn/Llama3-ChatQA-1.5-8B-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beratcmn/Llama3-ChatQA-1.5-8B-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/beratcmn/Llama3-ChatQA-1.5-8B-lora

SGLang

How to use beratcmn/Llama3-ChatQA-1.5-8B-lora 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 "beratcmn/Llama3-ChatQA-1.5-8B-lora" \
    --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": "beratcmn/Llama3-ChatQA-1.5-8B-lora",
		"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 "beratcmn/Llama3-ChatQA-1.5-8B-lora" \
        --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": "beratcmn/Llama3-ChatQA-1.5-8B-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use beratcmn/Llama3-ChatQA-1.5-8B-lora with Docker Model Runner:
```
docker model run hf.co/beratcmn/Llama3-ChatQA-1.5-8B-lora
```

Llama3-ChatQA-1.5-8B-lora

This is a LoRA extracted from a language model. It was extracted using mergekit.

LoRA Details

This LoRA adapter was extracted from nvidia/Llama3-ChatQA-1.5-8B and uses meta-llama/Meta-Llama-3-8B as a base.

Parameters

The following command was used to extract this LoRA adapter:

mergekit-extract-lora meta-llama/Meta-Llama-3-8B nvidia/Llama3-ChatQA-1.5-8B OUTPUT_PATH --no-lazy-unpickle --rank=64

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for beratcmn/Llama3-ChatQA-1.5-8B-lora

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

(607)

this model