--- language: - en license: apache-2.0 library_name: transformers tags: - chat - qwen - qwen2 - finetune - chatml - OpenHermes-2.5 - HelpSteer2 - Orca - SlimOrca base_model: Qwen/Qwen2-7B datasets: - nvidia/HelpSteer2 - teknium/OpenHermes-2.5 - microsoft/orca-math-word-problems-200k - Open-Orca/SlimOrca model_name: calme-2.7-qwen2-7b pipeline_tag: text-generation inference: false model_creator: MaziyarPanahi quantized_by: MaziyarPanahi model-index: - name: calme-2.7-qwen2-7b 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: 35.92 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.7-qwen2-7b 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: 28.91 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.7-qwen2-7b 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: 12.08 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.7-qwen2-7b 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: 5.48 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.7-qwen2-7b 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: 19.94 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.7-qwen2-7b 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: 30.06 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.7-qwen2-7b name: Open LLM Leaderboard --- Qwen2 fine-tune # MaziyarPanahi/calme-2.7-qwen2-7b This is a fine-tuned version of the `Qwen/Qwen2-7B` model. It aims to improve the base model across all benchmarks. # ⚡ Quantized GGUF All GGUF models are available here: [MaziyarPanahi/calme-2.7-qwen2-7b-GGUF](https://huggingface.co/MaziyarPanahi/calme-2.7-qwen2-7b-GGUF) # 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) coming soon! # Prompt Template This model uses `ChatML` prompt template: ``` <|im_start|>system {System} <|im_end|> <|im_start|>user {User} <|im_end|> <|im_start|>assistant {Assistant} ```` # How to use ```python # Use a pipeline as a high-level helper from transformers import pipeline messages = [ {"role": "user", "content": "Who are you?"}, ] pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.7-qwen2-7b") pipe(messages) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.7-qwen2-7b") model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.7-qwen2-7b") ``` # [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_MaziyarPanahi__calme-2.7-qwen2-7b) | Metric |Value| |-------------------|----:| |Avg. |22.07| |IFEval (0-Shot) |35.92| |BBH (3-Shot) |28.91| |MATH Lvl 5 (4-Shot)|12.08| |GPQA (0-shot) | 5.48| |MuSR (0-shot) |19.94| |MMLU-PRO (5-shot) |30.06|