--- language: - en license: apache-2.0 tags: - merge - fine-tuned datasets: - cognitivecomputations/dolphin - cognitivecomputations/dolphin-coder - ise-uiuc/Magicoder-OSS-Instruct-75K - teknium/openhermes - migtissera/Synthia-v1.3 base_model: - mistralai/Mistral-7B-Instruct-v0.2 - ehartford/dolphin-2.2.1-mistral-7b - SciPhi/SciPhi-Mistral-7B-32k - ehartford/samantha-1.2-mistral-7b - Arc53/docsgpt-7b-mistral - HuggingFaceH4/zephyr-7b-beta - meta-math/MetaMath-Mistral-7B - Open-Orca/Mistral-7B-OpenOrca - openchat/openchat-3.5-1210 - beowolx/MistralHermes-CodePro-7B-v1 - TIGER-Lab/MAmmoTH-7B-Mistral - teknium/OpenHermes-2.5-Mistral-7B - Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp - mlabonne/NeuralHermes-2.5-Mistral-7B model-index: - name: Mistral-7B-Merge-14-v0.3-ft-step-9984 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: 62.54 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 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: 82.18 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 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: 62.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 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: 53.7 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 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=EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 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: 25.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 name: Open LLM Leaderboard --- # Model Description This is fine-tuned model based on EmbeddedLLM/Mistral-7B-Merge-14-v0.3 for 9984 steps. The dataset used are: * dophin * dolphin-coder * Magicoder-OSS-Instruct-75K * openhermes * Synthia-v1.3 ## Chat Template Prompt format: This model uses ChatML prompt format. ``` <|im_start|>system You are Dolphin, a helpful AI assistant.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` # Training The model is scheduled to be fine-tuned for 3 epochs on 4 A100s using axolotl. # Shout-Out to OSS Thank you to the Open Source AI community for bringing together marvelous code frameworks and datasets. # [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_EmbeddedLLM__Mistral-7B-Merge-14-v0.3-ft-step-9984) | Metric |Value| |---------------------------------|----:| |Avg. |60.37| |AI2 Reasoning Challenge (25-Shot)|62.54| |HellaSwag (10-Shot) |82.18| |MMLU (5-Shot) |62.92| |TruthfulQA (0-shot) |53.70| |Winogrande (5-shot) |75.61| |GSM8k (5-shot) |25.25|