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
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| base_model: | |
| - mistralai/Mistral-7B-v0.1 | |
| - cognitivecomputations/dolphin-2.2.1-mistral-7b | |
| - Open-Orca/Mistral-7B-OpenOrca | |
| - openchat/openchat-3.5-0106 | |
| - mlabonne/NeuralHermes-2.5-Mistral-7B | |
| - GreenNode/GreenNode-mini-7B-multilingual-v1olet | |
| - berkeley-nest/Starling-LM-7B-alpha | |
| - viethq188/LeoScorpius-7B-Chat-DPO | |
| - meta-math/MetaMath-Mistral-7B | |
| - Intel/neural-chat-7b-v3-3 | |
| inference: false | |
| model-index: | |
| - name: Moza-7B-v1.0 | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: AI2 Reasoning Challenge (25-Shot) | |
| type: ai2_arc | |
| config: ARC-Challenge | |
| split: test | |
| args: | |
| num_few_shot: 25 | |
| metrics: | |
| - type: acc_norm | |
| value: 66.55 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: HellaSwag (10-Shot) | |
| type: hellaswag | |
| split: validation | |
| args: | |
| num_few_shot: 10 | |
| metrics: | |
| - type: acc_norm | |
| value: 83.45 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU (5-Shot) | |
| type: cais/mmlu | |
| config: all | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 62.77 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: TruthfulQA (0-shot) | |
| type: truthful_qa | |
| config: multiple_choice | |
| split: validation | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: mc2 | |
| value: 65.16 | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: Winogrande (5-shot) | |
| type: winogrande | |
| config: winogrande_xl | |
| split: validation | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 77.51 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GSM8k (5-shot) | |
| type: gsm8k | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 62.55 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 | |
| name: Open LLM Leaderboard | |
| # Moza-7B-v1.0 | |
|  | |
| This is a [meme-merge](https://en.wikipedia.org/wiki/Joke) of pre-trained language models, | |
| created using [mergekit](https://github.com/cg123/mergekit). | |
| Use at your own risk. | |
| ## Details | |
| ### Quantized Model | |
| - [GGUF](https://huggingface.co/kidyu/Moza-7B-v1.0-GGUF) | |
| ### Merge Method | |
| This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method, | |
| using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. | |
| The value for `density` are from [this blogpost](https://huggingface.co/blog/mlabonne/merge-models), | |
| 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: | |
| * [cognitivecomputations/dolphin-2.2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.2.1-mistral-7b) | |
| * [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) | |
| * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) | |
| * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) | |
| * [GreenNode/GreenNode-mini-7B-multilingual-v1olet](https://huggingface.co/GreenNode/GreenNode-mini-7B-multilingual-v1olet) | |
| * [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) | |
| * [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO) | |
| * [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) | |
| * [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) | |
| ### Prompt Format | |
| You can use `Alpaca` formatting for inference | |
| ``` | |
| ### Instruction: | |
| ### Response: | |
| ``` | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kidyu__Moza-7B-v1.0) | |
| | 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| | |