Instructions to use chargoddard/loyal-piano-m7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/loyal-piano-m7 with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/loyal-piano-m7")

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

tokenizer = AutoTokenizer.from_pretrained("chargoddard/loyal-piano-m7")
model = AutoModelForCausalLM.from_pretrained("chargoddard/loyal-piano-m7", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chargoddard/loyal-piano-m7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chargoddard/loyal-piano-m7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/loyal-piano-m7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chargoddard/loyal-piano-m7

SGLang

How to use chargoddard/loyal-piano-m7 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 "chargoddard/loyal-piano-m7" \
    --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": "chargoddard/loyal-piano-m7",
		"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 "chargoddard/loyal-piano-m7" \
        --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": "chargoddard/loyal-piano-m7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chargoddard/loyal-piano-m7 with Docker Model Runner:
```
docker model run hf.co/chargoddard/loyal-piano-m7
```

chargoddard commited on Dec 2, 2023

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@@ -12,6 +12,8 @@ tags:
 - mistral
 ---
 Experimenting with dataset ratios. Intended to be a roleplay-focused model with some smarts and good long-context recall.
 Not sure if I've succeeded on the roleplay front, but something sure went right! Currently the #4 7B model on the leaderboard as of 11/30/2023. Going to riff on this and see where it goes.

 - mistral
 ---
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 Experimenting with dataset ratios. Intended to be a roleplay-focused model with some smarts and good long-context recall.
 Not sure if I've succeeded on the roleplay front, but something sure went right! Currently the #4 7B model on the leaderboard as of 11/30/2023. Going to riff on this and see where it goes.