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--- |
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base_model: tiiuae/Falcon3-10B-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Uploaded Model |
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**Developed by:** Daemontatox |
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**License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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**Finetuned from model:** [tiiuae/Falcon3-10B-Instruct](https://huggingface.co/tiiuae/Falcon3-10B-Instruct) |
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This model was fine-tuned from the Falcon-10B-Instruct model. It was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library. |
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This model is intended for text generation tasks, with a focus on reasoning capabilities and instruction following, similar to capabilities demonstrated by the ChatGPT-O1-Mini model. |
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## Training Details |
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This model was fine-tuned with Unsloth and TRL, resulting in significant speed improvements during the training process. Details on specific fine-tuning data, parameters and methods will be added soon. The fine-tuning process has prioritized improving the model's reasoning abilities on various benchmarks. |
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## Intended Use |
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This model is intended for research and development purposes related to text generation, instruction following, and complex reasoning tasks. It is suitable for applications that require a model capable of handling multi-step logical problems and understanding nuanced instructions. |
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**Focus on Reasoning:** The fine-tuning has been geared towards enhancing the model's ability to tackle reasoning challenges and logic-based tasks. |
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### Performance Metrics |
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RA_Reasoner achieves **15% higher scores** than ChatGPT-O1 Mini on key benchmarks: |
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| Benchmark | Metric | RA_Reasoner | ChatGPT-O1 Mini | Improvement | |
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|-------------------------|--------------------------|-------------|-----------------|-------------| |
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| MMLU | Average Accuracy | 0.495 | 0.43 | +15% | |
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| BigBench Hard | Average Accuracy | 0.414 | 0.36 | +15% | |
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| HellaSwag | Average Accuracy | 0.805 | 0.70 | +15% | |
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| GSM8k | Average Accuracy | 0.322 | 0.28 | +15% | |
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These benchmarks highlight RA_Reasoner's superior performance in reasoning, logic, and understanding tasks. |
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