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--- |
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language: en |
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library_name: transformers |
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license: llama3.1 |
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base_model: meta-llama/Llama-3.1-8B-Instruct |
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pipeline_tag: text-generation |
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tags: |
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- llama-3.1 |
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- instruction-tuned |
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datasets: |
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- OpenAssistant/oasst1 |
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- databricks/databricks-dolly-15k |
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- Open-Orca/OpenOrca |
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- mlabonne/open-perfectblend |
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- tatsu-lab/alpaca |
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model-index: |
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- name: utkmst/chimera-beta-test2-lora-merged |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: leaderboard |
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name: Overall Leaderboard |
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metrics: |
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- name: acc_norm |
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type: acc_norm |
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value: 0.4440 |
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verified: true |
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- name: acc |
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type: acc |
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value: 0.2992 |
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verified: true |
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- name: exact_match |
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type: exact_match |
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value: 0.0951 |
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verified: true |
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- task: |
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type: text-generation |
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dataset: |
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type: bbh |
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name: BBH (Big Bench Hard) |
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metrics: |
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- name: acc_norm |
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type: acc_norm |
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value: 0.4773 |
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verified: true |
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- task: |
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type: text-generation |
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dataset: |
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type: gpqa |
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name: GPQA (Google-Patched Question Answering) |
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metrics: |
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- name: acc_norm |
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type: acc_norm |
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value: 0.3036 |
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verified: true |
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- task: |
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type: text-generation |
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dataset: |
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type: math |
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name: Math |
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metrics: |
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- name: exact_match |
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type: exact_match |
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value: 0.0951 |
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verified: true |
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- task: |
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type: text-generation |
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dataset: |
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type: mmlu_pro |
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name: MMLU-Pro |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.2992 |
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verified: true |
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- task: |
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type: text-generation |
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dataset: |
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type: musr |
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name: MUSR (Multi-Step Reasoning) |
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metrics: |
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- name: acc_norm |
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type: acc_norm |
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value: 0.4113 |
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verified: true |
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--- |
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# utkmst/chimera-beta-test2-lora-merged |
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## Model Description |
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This model is a fine-tuned version of Meta's Llama-3.1-8B-Instruct model, created through LoRA fine-tuning on multiple instruction datasets, followed by merging the adapter weights with the base model. |
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## Architecture |
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- **Base Model**: meta-llama/Llama-3.1-8B-Instruct |
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- **Size**: 8.03B parameters |
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- **Type**: Decoder-only transformer |
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- **Format**: SafeTensors (full precision) |
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## Training Details |
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- **Training Method**: LoRA fine-tuning followed by adapter merging |
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- **LoRA Configuration**: |
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- Rank: 8 |
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- Alpha: 16 |
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- Trainable modules: Attention layers and feed-forward networks |
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- **Training Hyperparameters**: |
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- Learning rate: 2e-4 |
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- Batch size: 2 |
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- Training epochs: 1 |
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- Optimizer: AdamW with constant scheduler |
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## Intended Use |
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This model is designed for: |
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- General purpose assistant capabilities |
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- Question answering and knowledge retrieval |
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- Creative content generation |
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- Instructional guidance |
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## Limitations |
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- Base model limitations including potential hallucinations and factual inaccuracies |
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- Limited context window compared to larger models |
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- Knowledge cutoff from the base Llama-3.1 model |
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- May exhibit biases present in training data |
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- Performance on specialized tasks may vary |
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## Usage with Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("utkmst/chimera-beta-test2-lora-merged") |
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tokenizer = AutoTokenizer.from_pretrained("utkmst/chimera-beta-test2-lora-merged") |
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``` |
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## License |
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This model inherits the license from Meta's Llama 3.1. |