--- license: apache-2.0 library_name: peft tags: - code - instruct - mistral datasets: - HuggingFaceH4/no_robots base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral_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: 58.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_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: 80.57 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_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: 57.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_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: 41.91 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_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: 75.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_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: 38.36 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=qblocks/mistral_7b_norobots name: Open LLM Leaderboard --- ### Finetuning Overview: **Model Used:** mistralai/Mistral-7B-v0.1 **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 [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning: - Was achieved with great cost-effectiveness. - Completed in a total duration of 1h 15m 3s for 2 epochs using an A6000 48GB GPU. - Costed `$2.525` for the entire 2 epochs. #### Hyperparameters & Additional Details: - **Epochs:** 2 - **Cost Per Epoch:** $1.26 - **Total Finetuning Cost:** $2.525 - **Model Path:** mistralai/Mistral-7B-v0.1 - **Learning Rate:** 0.0002 - **Data Split:** 100% train - **Gradient Accumulation Steps:** 64 - **lora r:** 64 - **lora alpha:** 16 #### Prompt Structure ``` <|system|> <|user|> [USER PROMPT] <|assistant|> [ASSISTANT ANSWER] ``` #### Train loss : ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/Badi_wgZLBsUdeIScEKs9.png) ### Benchmarking results : ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6313732454e6e5d9f0f797cd/ialM-cJygMgMgczskzicX.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__mistral_7b_norobots) | Metric |Value| |---------------------------------|----:| |Avg. |58.85| |AI2 Reasoning Challenge (25-Shot)|58.96| |HellaSwag (10-Shot) |80.57| |MMLU (5-Shot) |57.66| |TruthfulQA (0-shot) |41.91| |Winogrande (5-shot) |75.61| |GSM8k (5-shot) |38.36|