Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
Eval Results (legacy)
text-generation-inference
Instructions to use kidyu/Moza-7B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("kidyu/Moza-7B-v1.0") model = AutoModelForMultimodalLM.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
| 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 |