--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO base_model: - Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO - Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO model-index: - name: Phi3mash1-17B-pass results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 18.84 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Phi3mash1-17B-pass name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 45.25 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Phi3mash1-17B-pass name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 0.0 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Phi3mash1-17B-pass name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 9.28 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Phi3mash1-17B-pass name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 14.84 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Phi3mash1-17B-pass name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 39.88 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Phi3mash1-17B-pass name: Open LLM Leaderboard --- # Phi3-19B-pass Phi3-19B-pass is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO](https://huggingface.co/Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO) * [Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO](https://huggingface.co/Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO) ## 🧩 Configuration ```yaml slices: - sources: - model: Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO layer_range: [0, 24] - sources: - model: Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO layer_range: [8, 32] merge_method: passthrough dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "allknowingroger/Phi3-19B-pass" 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/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Phi3mash1-17B-pass) | Metric |Value| |-------------------|----:| |Avg. |21.35| |IFEval (0-Shot) |18.84| |BBH (3-Shot) |45.25| |MATH Lvl 5 (4-Shot)| 0.00| |GPQA (0-shot) | 9.28| |MuSR (0-shot) |14.84| |MMLU-PRO (5-shot) |39.88|