Instructions to use ludis/tsukasa-7b-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ludis/tsukasa-7b-qlora with Transformers:

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

pipe = pipeline("text-generation", model="ludis/tsukasa-7b-qlora")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ludis/tsukasa-7b-qlora")
model = AutoModelForCausalLM.from_pretrained("ludis/tsukasa-7b-qlora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ludis/tsukasa-7b-qlora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ludis/tsukasa-7b-qlora

SGLang

How to use ludis/tsukasa-7b-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 "ludis/tsukasa-7b-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": "ludis/tsukasa-7b-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 "ludis/tsukasa-7b-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": "ludis/tsukasa-7b-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ludis/tsukasa-7b-qlora with Docker Model Runner:
```
docker model run hf.co/ludis/tsukasa-7b-qlora
```

tsukasa-7b-qlora / README.md

ludis

Update README.md

ffbed99 verified almost 3 years ago

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metadata

datasets:
  - PygmalionAI/PIPPA
  - lemonilia/LimaRP

Gen Settings & Prompting

https://rentry.org/tsukasamodel

Training

axolotl was used for training on a 8x nvidia a40 gpu cluster.

the a40 GPU cluster has been graciously provided by Arc Compute.

rank 16 qlora (all modules) tune

base model mistralai/Mistral-7B-v0.1 tuned on koishi commit 6e675d1 for one epoch

then tuned on pippa 6412b0c for one epoch (metharme completion)

then tuned on limarp Version 2023-10-19 for 2 epochs in metharme completion format