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
```

ludis commited on Feb 1, 2024

Commit

ffbed99

verified ·

1 Parent(s): 5fcf89c

Update README.md

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README.md +4 -6

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@@ -4,6 +4,10 @@ datasets:
   - lemonilia/LimaRP
 ---
 ## Training
 [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
@@ -18,9 +22,3 @@ base model mistralai/Mistral-7B-v0.1 tuned on koishi commit 6e675d1 for one epoc
 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
-## Prompting
-The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
-The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.

   - lemonilia/LimaRP
 ---
+## Gen Settings & Prompting
+https://rentry.org/tsukasamodel
 ## Training
 [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
 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