--- license: apache-2.0 tags: - merge --- # openmixtral-6x7b-v2 Quantized openmixtral-6x7b-merged_v2 is a merge of the following 6x7B models: ## 🧩 Configuration ```yaml base_model: mlabonne/Marcoro14-7B-slerp experts: - source_model: openchat/openchat-3.5-1210 positive_prompts: - "chat" - "assistant" - "tell me" - "explain" - source_model: Weyaxi/Einstein-v4-7B positive_prompts: - "physics" - "biology" - "chemistry" - "science" - source_model: BioMistral/BioMistral-7B positive_prompts: - "medical" - "pubmed" - "healthcare" - "health" - source_model: beowolx/CodeNinja-1.0-OpenChat-7B positive_prompts: - "code" - "python" - "javascript" - "programming" - "algorithm" - source_model: maywell/PiVoT-0.1-Starling-LM-RP positive_prompts: - "storywriting" - "write" - "scene" - "story" - "character" - source_model: WizardLM/WizardMath-7B-V1.1 positive_prompts: - "reason" - "math" - "mathematics" - "solve" - "count" tokenizer_source: union ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "mychen76/openmixtral-6x7b-v2" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Why the sky is blue"}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [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_mychen76__openmixtral-6x7b-v2) | Metric |Value| |---------------------------------|----:| |Avg. |72.33| |AI2 Reasoning Challenge (25-Shot)|68.52| |HellaSwag (10-Shot) |86.75| |MMLU (5-Shot) |65.11| |TruthfulQA (0-shot) |65.13| |Winogrande (5-shot) |79.87| |GSM8k (5-shot) |68.61|