Instructions to use kidyu/Moza-7B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kidyu/Moza-7B-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="kidyu/Moza-7B-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kidyu/Moza-7B-v1.0")
model = AutoModelForCausalLM.from_pretrained("kidyu/Moza-7B-v1.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kidyu/Moza-7B-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kidyu/Moza-7B-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kidyu/Moza-7B-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kidyu/Moza-7B-v1.0

SGLang

How to use kidyu/Moza-7B-v1.0 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 "kidyu/Moza-7B-v1.0" \
    --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": "kidyu/Moza-7B-v1.0",
		"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 "kidyu/Moza-7B-v1.0" \
        --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": "kidyu/Moza-7B-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kidyu/Moza-7B-v1.0 with Docker Model Runner:
```
docker model run hf.co/kidyu/Moza-7B-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Moza-7B-v1.0

This is a meme-merge of pre-trained language models, created using mergekit. Use at your own risk.

Details

Quantized Model

GGUF

Merge Method

This model was merged using the DARE TIES merge method, using mistralai/Mistral-7B-v0.1 as a base.

The value for density are from this blogpost, and the weight was randomly generated and then assigned to the models, with priority (of using the bigger weight) to NeuralHermes, OpenOrca, and neural-chat. The models themselves are chosen by "vibes".

Models Merged

The following models were included in the merge:

Prompt Format

You can use Alpaca formatting for inference

### Instruction:

### Response:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mistral-7B-v0.1
models:
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.63
      weight: 0.83
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      density: 0.63
      weight: 0.74
  - model: meta-math/MetaMath-Mistral-7B
    parameters:
      density: 0.63
      weight: 0.22
  - model: openchat/openchat-3.5-0106
    parameters:
      density: 0.63
      weight: 0.37
  - model: Open-Orca/Mistral-7B-OpenOrca
    parameters:
      density: 0.63
      weight: 0.76
  - model: cognitivecomputations/dolphin-2.2.1-mistral-7b
    parameters:
      density: 0.63
      weight: 0.69
  - model: viethq188/LeoScorpius-7B-Chat-DPO
    parameters:
      density: 0.63
      weight: 0.38
  - model: GreenNode/GreenNode-mini-7B-multilingual-v1olet
    parameters:
      density: 0.63
      weight: 0.13
  - model: berkeley-nest/Starling-LM-7B-alpha
    parameters:
      density: 0.63
      weight: 0.33
merge_method: dare_ties
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.66
AI2 Reasoning Challenge (25-Shot)	66.55
HellaSwag (10-Shot)	83.45
MMLU (5-Shot)	62.77
TruthfulQA (0-shot)	65.16
Winogrande (5-shot)	77.51
GSM8k (5-shot)	62.55