--- license: mit tags: - merge - mergekit - lazymergekit - bardsai/jaskier-7b-dpo-v6.1 - CultriX/NeuralTrix-7B-dpo base_model: - bardsai/jaskier-7b-dpo-v6.1 - CultriX/NeuralTrix-7B-dpo model-index: - name: NeuralJaskier-7b-dpo 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: 71.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=u66u/NeuralJaskier-7b-dpo 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: 88.87 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=u66u/NeuralJaskier-7b-dpo 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: 64.49 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=u66u/NeuralJaskier-7b-dpo 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: 78.42 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=u66u/NeuralJaskier-7b-dpo 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: 84.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=u66u/NeuralJaskier-7b-dpo 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: 69.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=u66u/NeuralJaskier-7b-dpo name: Open LLM Leaderboard --- # NeuralJaskier-7b-dpo NeuralJaskier-7b-dpo is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [bardsai/jaskier-7b-dpo-v6.1](https://huggingface.co/bardsai/jaskier-7b-dpo-v6.1) * [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) ## 🧩 Configuration ```yaml slices: - sources: - model: bardsai/jaskier-7b-dpo-v6.1 layer_range: [0, 32] - model: CultriX/NeuralTrix-7B-dpo layer_range: [0, 32] merge_method: slerp base_model: bardsai/jaskier-7b-dpo-v6.1 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 = "u66u/NeuralJaskier-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=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_u66u__NeuralJaskier-7b-dpo) | Metric |Value| |---------------------------------|----:| |Avg. |76.22| |AI2 Reasoning Challenge (25-Shot)|71.59| |HellaSwag (10-Shot) |88.87| |MMLU (5-Shot) |64.49| |TruthfulQA (0-shot) |78.42| |Winogrande (5-shot) |84.45| |GSM8k (5-shot) |69.52|