--- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - fblgit/UNA-TheBeagle-7b-v1 - argilla/distilabeled-Marcoro14-7B-slerp base_model: - fblgit/UNA-TheBeagle-7b-v1 - argilla/distilabeled-Marcoro14-7B-slerp --- # Beagle14-7B **Update 01/16/24: Check the DPO fine-tuned version of this model, [NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) (probably the best 7B model you can find)! 🎉** Beagle14-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) * [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) ## 🏆 Evaluation The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |----------------------------------------------------------|------:|------:|---------:|-------:|------:| |[**Beagle14-7B**](https://huggingface.co/mlabonne/Beagle14-7B)| **44.38**| **76.53**| **69.44**| **47.25**| **59.4**| |[OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)| 42.75| 72.99| 52.99| 40.94| 52.42| |[NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)| 43.67| 73.24| 55.37| 41.76| 53.51| |[Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)| 47.79| 74.69| 55.92| 44.84| 55.81| |[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67| |[CatMarcoro14-7B-slerp](https://huggingface.co/occultml/CatMarcoro14-7B-slerp)| 45.21| 75.91| 63.81| 47.31| 58.06| ## 🧩 Configuration ```yaml slices: - sources: - model: fblgit/UNA-TheBeagle-7b-v1 layer_range: [0, 32] - model: argilla/distilabeled-Marcoro14-7B-slerp layer_range: [0, 32] merge_method: slerp base_model: fblgit/UNA-TheBeagle-7b-v1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Beagle14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) 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"]) ```