--- license: mit model-index: - name: NeuralHermes-2.5-Mistral-7B-distilabel 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: 65.78 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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: 84.97 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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: 63.63 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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: 55.86 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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: 78.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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: 61.49 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel name: Open LLM Leaderboard --- Experiment with distilabel: ```python dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train", token=hf_token) dataset = dataset.filter(lambda r: r["status"]!="tie" and r["chosen_score"]>5) def chatml_format(example): # Format system if len(example['system']) > 0: message = {"role": "system", "content": example['system']} system = tokenizer.apply_chat_template([message], tokenize=False) else: system = "" # Format instruction message = {"role": "user", "content": example['input']} prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True) # Format chosen answer chosen = example['chosen'] + "<|im_end|>\n" # Format rejected answer rejected = example['rejected'] + "<|im_end|>\n" return { "prompt": system + prompt, "chosen": chosen, "rejected": rejected, } # Load dataset #dataset = load_dataset("Intel/orca_dpo_pairs")['train'] # Save columns original_columns = dataset.column_names # Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" # Format dataset dataset = dataset.map( chatml_format, remove_columns=original_columns ) # Print sample dataset[1] ``` # [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_dvilasuero__NeuralHermes-2.5-Mistral-7B-distilabel) | Metric |Value| |---------------------------------|----:| |Avg. |68.40| |AI2 Reasoning Challenge (25-Shot)|65.78| |HellaSwag (10-Shot) |84.97| |MMLU (5-Shot) |63.63| |TruthfulQA (0-shot) |55.86| |Winogrande (5-shot) |78.69| |GSM8k (5-shot) |61.49|