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
```

loyal-piano-m7 / README.md

chargoddard

Update README.md

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	---
	license: cc-by-nc-4.0
	datasets:
	- pankajmathur/orca_mini_v1_dataset
	- openai/summarize_from_feedback
	- PygmalionAI/PIPPA
	- chargoddard/rpguild
	- lemonilia/LimaRP
	language:
	- en
	tags:
	- 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.

	\| model \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Winogrande \| GSM8K \| DROP \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| fblgit/juanako-7b-UNA \| 59.91 \| 68.17 \| 85.34 \| 62.47 \| 65.13 \| 78.85 \| 20.7 \| 38.74 \|
	\| Intel/neural-chat-7b-v3-1 \| 59.06 \| 66.21 \| 83.64 \| 62.37 \| 59.65 \| 78.14 \| 19.56 \| 43.84 \|
	\| Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B \| 58.6 \| 66.55 \| 84.47 \| 63.34 \| 61.22 \| 78.37 \| 23.58 \| 32.66 \|
	\| chargoddard/loyal-piano-m7 \| 58.42 \| 66.72 \| 85.03 \| 64.43 \| 60.03 \| 79.08 \| 25.7 \| 27.92 \|
	\| Gryphe/MythoMist7b \| 58.26 \| 65.87 \| 83.55 \| 62.32 \| 59.98 \| 78.06 \| 20.24 \| 37.82 \|


	Dataset composition:
	\| dataset \| rows used \| percent of total \|
	\| --- \| --- \| --- \|
	\| PIPPA \| 14.6k \| 43% \|
	\| summarize_from_feedback \| 9k \| 26% \|
	\| orca_mini_v1_dataset \| 5.6k \| 17% \|
	\| rpguild \| 2.86k \| 8% \|
	\| LimaRP \| 2k \| 6% \|