--- license: apache-2.0 --- --- tags: - merge - mergekit - '#dpo' - MaximeLabonne - '#mergeofmerge' base_model: - CultriX/NeuralTrix-7B-dpo - paulml/OmniBeagleSquaredMBX-v3-7B-v2 license: apache-2.0 --- # This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! [Labonne LLM Course](https://github.com/mlabonne/llm-course)  # NeuTrixOmniBe-DPO NeuTrix7000-7b-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml MODEL_NAME = "NeuTrix7000-7b-DPO" yaml_config = """ slices: - sources: - model: CultriX/NeuralTrix-7B-dpo layer_range: [0, 32] - model: paulml/OmniBeagleSquaredMBX-v3-7B-v2 layer_range: [0, 32] merge_method: slerp base_model: CultriX/NeuralTrix-7B-dpo 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 """ ``` It was then trained with DPO using: * Intel/orca_dpo_pairs ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/NeuTrix7000-7b-DPO" 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=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95) print(outputs[0]["generated_text"])