--- license: apache-2.0 library_name: peft tags: - code - instruct - zephyr datasets: - HuggingFaceH4/no_robots base_model: HuggingFaceH4/zephyr-7b-alpha model-index: - name: zephyr_7b_norobots 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: 56.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/zephyr_7b_norobots 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: 79.64 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/zephyr_7b_norobots 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: 55.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/zephyr_7b_norobots 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: 44.6 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/zephyr_7b_norobots 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: 74.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/zephyr_7b_norobots 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: 20.62 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/zephyr_7b_norobots name: Open LLM Leaderboard --- ### Finetuning Overview: **Model Used:** HuggingFaceH4/zephyr-7b-alpha **Dataset:** HuggingFaceH4/no_robots #### Dataset Insights: [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. #### Finetuning Details: With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning: - Was achieved with great cost-effectiveness. - Completed in a total duration of 36mins 47secs for 1 epoch using an A6000 48GB GPU. - Costed `$1.212` for the entire epoch. #### Hyperparameters & Additional Details: - **Epochs:** 1 - **Cost Per Epoch:** $1.212 - **Total Finetuning Cost:** $1.212 - **Model Path:** HuggingFaceH4/zephyr-7b-alpha - **Learning Rate:** 0.0002 - **Data Split:** 100% train - **Gradient Accumulation Steps:** 4 - **lora r:** 32 - **lora alpha:** 64 --- #### Prompt Structure ``` <|system|> <|endoftext|> <|user|> [USER PROMPT]<|endoftext|> <|assistant|> [ASSISTANT ANSWER] <|endoftext|> ``` #### Train loss : ![training loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/EYUTlcPFz-2nXzNj5_gsW.png) license: apache-2.0 # [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_qblocks__zephyr_7b_norobots) | Metric |Value| |---------------------------------|----:| |Avg. |55.16| |AI2 Reasoning Challenge (25-Shot)|56.48| |HellaSwag (10-Shot) |79.64| |MMLU (5-Shot) |55.52| |TruthfulQA (0-shot) |44.60| |Winogrande (5-shot) |74.11| |GSM8k (5-shot) |20.62|