Instructions to use SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE with Transformers:

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

pipe = pipeline("text-generation", model="SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE")
model = AutoModelForCausalLM.from_pretrained("SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE

SGLang

How to use SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE 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 "SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE" \
    --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": "SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE",
		"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 "SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE" \
        --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": "SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE with Docker Model Runner:
```
docker model run hf.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Description

This repository hosts FP16 files for Loyal-Toppy-Bruins-Maid-7B, a 7B model aimed at having engaging RP with solid character card adherence and being a smart cookie at the same time.

Its foundation is Starling-LM-7B-alpha, notable for its performance in the LMSYS Chatbot Arena, even surpassing GPT-3.5-Turbo-1106. The model incorporates rwitz/go-bruins-v2, a Q-bert/MetaMath-Cybertron-Starling derivative with Alpaca RP data tuning.

The other foundational model is chargoddard/loyal-piano-m7, chosen for its strong RP performance and Alpaca format training, with a diverse dataset including PIPPA, rpbuild, and LimaRP.

Undi95/Toppy-M-7B, known for its creativity, brings in useful RP data from various sources. It ranks first among 7B models on OpenRouter for a good reason.

NeverSleep/Noromaid-7b-v0.1.1, a Mistral finetune with unique RP data not present in other models, was also added for bringing in a unique RP dataset and being a well-regarded RP model.

The models were merged using the DARE ties method, with a targeted 1.2 absolute weight and high density (0.5-0.6), as discussed in the MergeKit GitHub Repo.

Currently, this model ranks at the top of my personal RP unit test benchmark and scored a very solid 20 on lilblam's LLM Logic Test. My first impressions of it for RPing are very good but, admittedly, this model came out of the oven today so I haven't played it with it too much 😊

The sauce

models: # Top-Loyal-Bruins-Maid-DARE-7B_v2
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: rwitz/go-bruins-v2 # MetamathCybertronStarling base
    parameters:
      weight: 0.5
      density: 0.6
  - model: chargoddard/loyal-piano-m7 # Pull in some PIPPA/LimaRP/Orca/rpguild
    parameters:
      weight: 0.5
      density: 0.6
  - model: Undi95/Toppy-M-7B
    parameters:
      weight: 0.1
      density: 0.5
  - model: NeverSleep/Noromaid-7b-v0.1.1
    parameters:
      weight: 0.1
      density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

Prompt template: Custom format, or Alpaca

Custom format:

I found the best SillyTavern results from using the Noromaid template.

SillyTavern config files: Context, Instruct.

Otherwise, I tried to ensure that all of the underlying merged models were Alpaca favored.

Alpaca:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Downloads last month: 80

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE

Merges

6 models

Quantizations

1 model