Instructions to use ToastyPigeon/BlackMagic-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToastyPigeon/BlackMagic-7B with Transformers:

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

pipe = pipeline("text-generation", model="ToastyPigeon/BlackMagic-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ToastyPigeon/BlackMagic-7B")
model = AutoModelForCausalLM.from_pretrained("ToastyPigeon/BlackMagic-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ToastyPigeon/BlackMagic-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ToastyPigeon/BlackMagic-7B

SGLang

How to use ToastyPigeon/BlackMagic-7B 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 "ToastyPigeon/BlackMagic-7B" \
    --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": "ToastyPigeon/BlackMagic-7B",
		"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 "ToastyPigeon/BlackMagic-7B" \
        --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": "ToastyPigeon/BlackMagic-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

merge

This is a merge of pre-trained language models created using mergekit.

Thank you to MatchaSpeed for picking out the models to go into this merge. You're awesome! 🤗

The models in this merge were chosen for their attention to detail and/or their creativity.

Based on each model's individual strength, weights were applied to prioritize the model's intelligence (self-attention weights) or style/creativity (multi-layer perceptron weights).

GGUF versions available courtesy of BackyardAI https://huggingface.co/backyardai/BlackMagic-7B-GGUF

Usage

Recommended prompt format is Alpaca, as this is most widely accepted by the component models.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using Mistral-7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

fearlessdots/WizardLM-2-7B-abliterated
Gryphe/MythoMist-7b
Sao10K/Frostwind-v2.1-m7
senseable/Westlake-7b-v2
maywell/PiVoT-0.1-Evil-a
Undi95/Toppy-M-7B

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: fearlessdots/WizardLM-2-7B-abliterated
    parameters:
      weight: 1.0
  - model: Undi95/Toppy-M-7B
    parameters:
      weight: 
        - filter: self_attn
          value: 0.8
        - value: 0.5
  - model: senseable/Westlake-7b-v2
    parameters:
      weight:
        - filter: self_attn
          value: 0.6
        - value: 0.4
  - model: maywell/PiVoT-0.1-Evil-a
    parameters:
      weight: 
        - filter: mlp
          value: 0.2
        - value: 0.0
  - model: Sao10K/Frostwind-v2.1-m7
    parameters:
      weight: 
        - filter: self_attn
          value: 0.2
        - filter: mlp
          value: 0.8
        - value: 0.5
  - model: Gryphe/MythoMist-7b
    parameters:
      weight: 
        - filter: mlp
          value: 0.6
        - value: 0.0
base_model: TeeZee/Mistral-7B-v0.1-fp32
merge_method: task_arithmetic
dtype: float32