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--- |
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base_model: Daemontatox/PathFinderAI3.0 |
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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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- qwen2 |
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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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model-index: |
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- name: PathFinderAi3.0 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: wis-k/instruction-following-eval |
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split: train |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 42.71 |
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name: averaged accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: SaylorTwift/bbh |
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split: test |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 55.54 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: lighteval/MATH-Hard |
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split: test |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 48.34 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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split: train |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 21.14 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 20.05 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 52.86 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 |
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name: Open LLM Leaderboard |
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--- |
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# PathFinderAI 3.0 |
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PathFinderAI 3.0 is a high-performance language model designed for advanced reasoning, real-time text analysis, and decision support. Fine-tuned for diverse applications, it builds upon the capabilities of Qwen2, optimized with cutting-edge tools for efficiency and performance. |
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## Features |
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- **Advanced Reasoning:** Fine-tuned for real-time problem-solving and logic-driven tasks. |
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- **Enhanced Performance:** Trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and the Hugging Face TRL library. |
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- **Multi-domain Capability:** Excels in education, research, business, legal, and healthcare applications. |
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- **Optimized Architecture:** Leverages Qwen2 for robust language understanding and generation. |
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## Training Details |
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- **Base Model:** Daemontatox/PathFinderAI3.0 |
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- **Training Frameworks:** [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face’s TRL library. |
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- **Optimization:** Quantization-aware training for faster inference and deployment on resource-constrained environments. |
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## Deployment |
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This model is ideal for deployment on both cloud platforms and edge devices, including Raspberry Pi, utilizing efficient quantization techniques. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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## License |
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The model is open-sourced under the Apache 2.0 license. |
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## Usage |
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To load the model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Daemontatox/PathFinderAI3.0" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Example usage |
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input_text = "What is the capital of France?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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Model Applications |
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PathFinderAI 3.0 is designed for: |
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Real-time reasoning and problem-solving |
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Text generation and comprehension |
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Legal and policy analysis |
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Educational tutoring |
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Healthcare decision support |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Daemontatox__PathFinderAi3.0-details)! |
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Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox%2FPathFinderAi3.0&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! |
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| Metric |Value (%)| |
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|-------------------|--------:| |
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|**Average** | 40.11| |
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|IFEval (0-Shot) | 42.71| |
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|BBH (3-Shot) | 55.54| |
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|MATH Lvl 5 (4-Shot)| 48.34| |
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|GPQA (0-shot) | 21.14| |
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|MuSR (0-shot) | 20.05| |
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|MMLU-PRO (5-shot) | 52.86| |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Daemontatox__PathFinderAi3.0-details)! |
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Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox%2FPathFinderAi3.0&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! |
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| Metric |Value (%)| |
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|-------------------|--------:| |
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|**Average** | 40.11| |
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|IFEval (0-Shot) | 42.71| |
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|BBH (3-Shot) | 55.54| |
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|MATH Lvl 5 (4-Shot)| 48.34| |
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|GPQA (0-shot) | 21.14| |
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|MuSR (0-shot) | 20.05| |
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|MMLU-PRO (5-shot) | 52.86| |
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