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

9ad4f1c almost 3 years ago

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metadata

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

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%