Instructions to use fangloveskari/ORCA_LLaMA_70B_QLoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fangloveskari/ORCA_LLaMA_70B_QLoRA with Transformers:

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

pipe = pipeline("text-generation", model="fangloveskari/ORCA_LLaMA_70B_QLoRA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fangloveskari/ORCA_LLaMA_70B_QLoRA")
model = AutoModelForCausalLM.from_pretrained("fangloveskari/ORCA_LLaMA_70B_QLoRA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fangloveskari/ORCA_LLaMA_70B_QLoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fangloveskari/ORCA_LLaMA_70B_QLoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fangloveskari/ORCA_LLaMA_70B_QLoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fangloveskari/ORCA_LLaMA_70B_QLoRA

SGLang

How to use fangloveskari/ORCA_LLaMA_70B_QLoRA 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 "fangloveskari/ORCA_LLaMA_70B_QLoRA" \
    --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": "fangloveskari/ORCA_LLaMA_70B_QLoRA",
		"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 "fangloveskari/ORCA_LLaMA_70B_QLoRA" \
        --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": "fangloveskari/ORCA_LLaMA_70B_QLoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fangloveskari/ORCA_LLaMA_70B_QLoRA with Docker Model Runner:
```
docker model run hf.co/fangloveskari/ORCA_LLaMA_70B_QLoRA
```

About QLoRA Implementation

by II-Matto - opened Sep 1, 2023

Discussion

II-Matto

Sep 1, 2023

Hi, thanks for sharing the great work! I was wondering what is the QLoRA implementation you use for the fine-tuning. Do you use the official QLoRA code (https://github.com/artidoro/qlora/tree/main) or you implement it by yourself? Besides, could you describe the hardware settings and the corresponding training speed?

fangloveskari

Owner Sep 2, 2023

nop, I use https://github.com/hiyouga/LLaMA-Efficient-Tuning.
I use deepspeed ZERO-2 + FlashAttention2 + 4bit QLoRA to training with 8 A100(80G), then the batch size can be set to around 16.
as for training speed, It may take 5-6 hours for training? I'm not really clear with that.

btw, Our team tends to upload a better model and details about the dataset mix-up strategy, the hardware setting, the training settings and steps will also be provided, but it may take several days for that.

II-Matto

Sep 2, 2023

Thanks for your prompt reply. I am anticipating your following work!

II-Matto changed discussion status to closed Sep 2, 2023

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