--- license: apache-2.0 datasets: - databricks/databricks-dolly-15k pipeline_tag: text-generation model-index: - name: Instruct_Mixtral-8x7B-v0.1_Dolly15K 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: 69.28 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Brillibits/Instruct_Mixtral-8x7B-v0.1_Dolly15K 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: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Brillibits/Instruct_Mixtral-8x7B-v0.1_Dolly15K 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: 70.96 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Brillibits/Instruct_Mixtral-8x7B-v0.1_Dolly15K 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: 64.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Brillibits/Instruct_Mixtral-8x7B-v0.1_Dolly15K 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: 82.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Brillibits/Instruct_Mixtral-8x7B-v0.1_Dolly15K 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: 59.44 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Brillibits/Instruct_Mixtral-8x7B-v0.1_Dolly15K name: Open LLM Leaderboard --- # Instruct_Mixtral-8x7B-v0.1_Dolly15K Fine-tuned from Mixtral-8x7B-v0.1, used Dolly15k for the dataset. 85% for training, 14.9% validation, 0.1% test. Trained for 1.0 epochs using QLora. Trained with 1024 context window. # Model Details * **Trained by**: trained by [Brillibits](https://www.youtube.com/@Brillibits). * **Model type:** **Instruct_Mixtral-8x7B-v0.1_Dolly15K** is an auto-regressive language model based on the Llama 2 transformer architecture. * **Language(s)**: English * **License for Instruct_Mixtral-8x7B-v0.1_Dolly15K**: apache-2.0 license # Prompting ## Prompt Template With Context ``` Write a 10-line poem about a given topic Input: The topic is about racecars Output: ``` ## Prompt Template Without Context ``` Who was the was the second president of the United States? Output: ``` ## Professional Assistance This model and other models like it are great, but where LLMs hold the most promise is when they are applied on custom data to automate a wide variety of tasks If you have a dataset and want to see if you might be able to apply that data to automate some tasks, and you are looking for professional assistance, contact me [here](mailto:blakecmallory@gmail.com) # [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_Brillibits__Instruct_Mixtral-8x7B-v0.1_Dolly15K) | Metric |Value| |---------------------------------|----:| |Avg. |72.44| |AI2 Reasoning Challenge (25-Shot)|69.28| |HellaSwag (10-Shot) |87.59| |MMLU (5-Shot) |70.96| |TruthfulQA (0-shot) |64.83| |Winogrande (5-shot) |82.56| |GSM8k (5-shot) |59.44|