Instructions to use deinon-daemon/axolotl-13b-chat-qlora-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deinon-daemon/axolotl-13b-chat-qlora-dev with Transformers:

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

pipe = pipeline("text-generation", model="deinon-daemon/axolotl-13b-chat-qlora-dev")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deinon-daemon/axolotl-13b-chat-qlora-dev")
model = AutoModelForCausalLM.from_pretrained("deinon-daemon/axolotl-13b-chat-qlora-dev")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deinon-daemon/axolotl-13b-chat-qlora-dev with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deinon-daemon/axolotl-13b-chat-qlora-dev"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deinon-daemon/axolotl-13b-chat-qlora-dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deinon-daemon/axolotl-13b-chat-qlora-dev

SGLang

How to use deinon-daemon/axolotl-13b-chat-qlora-dev 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 "deinon-daemon/axolotl-13b-chat-qlora-dev" \
    --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": "deinon-daemon/axolotl-13b-chat-qlora-dev",
		"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 "deinon-daemon/axolotl-13b-chat-qlora-dev" \
        --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": "deinon-daemon/axolotl-13b-chat-qlora-dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deinon-daemon/axolotl-13b-chat-qlora-dev with Docker Model Runner:
```
docker model run hf.co/deinon-daemon/axolotl-13b-chat-qlora-dev
```

¡ Say hi to Axolotl ! Say hello to axolotl: a small-is-powerful instruct-tuned chat model! This is my second build ever in the fine tuning world. It was hacked in about 48hrs, and was executed entirely on one colab kernel for ~8-9hrs last night (07/29/23) ... enjoy! Test run of Llama-2-13b-chat-hf fine tuned using recently popularized quantized PEFT approach: used Bitsandbytes, --bf16, QLORA, Flash Attn w/ einops and ninja Ampere optimizations, 1 Nvidia A100 GPU for ~9hrs. Fine tuned for 3 epochs on a 40k slice of the Open-Orca dataset, which I postprocessed, added some self-collected contextual qa chat data to, and templated to yield a standard chat instruct prompt format for all examples. Benchmarks at least as good (if not slightly better) than other fine tuned llama/alpaca/guanaco/vicuna models of this scale. The real evaulation/benchmarking is still to come, however, specifically against stabilityai/StableBeluga13B, which seems to be the most popular example of Llama-2 + Open-Orca to date. This is simply a proof of concept (hence the dev tag) -- come back later once we've realeased a model for production.

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