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--- |
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license: apache-2.0 |
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datasets: |
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- Locutusque/hercules-v5.0 |
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base_model: M4-ai/Hercules-5.0-Qwen2-1.5B |
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language: |
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- en |
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inference: |
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parameters: |
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do_sample: true |
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temperature: 0.8 |
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top_p: 0.95 |
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top_k: 40 |
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min_p: 0.1 |
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max_new_tokens: 250 |
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repetition_penalty: 1.1 |
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pipeline_tag: text-generation |
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--- |
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# Hercules-5.0-Qwen2-1.5B-GGUF |
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This is quantized version of [M4-ai/Hercules-5.0-Qwen2-1.5B](https://huggingface.co/M4-ai/Hercules-5.0-Qwen2-1.5B) created using llama.cpp |
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# Model Description |
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<!-- Provide a quick summary of what the model is/does. --> |
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We fine-tuned qwen2-1.5B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format. |
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## Model Details |
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<!-- Provide a longer summary of what this model is. --> |
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This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants. |
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- **Developed by:** M4-ai |
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- **Language(s) (NLP):** English and maybe Chinese |
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- **License:** apache-2.0 |
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- **Finetuned from model:** [qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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General purpose assistant, question answering, chain-of-thought, etc.. |
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This language model made an impressive achievement, and correctly implemented a Multi Head Attention for use in a transformer neural network. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## Training Details |
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### Training Data |
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- Locutusque/hercules-v5.0 |
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## Evaluations |
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coming soon |
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#### Training Hyperparameters |
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- **Training regime:** bf16 non-mixed precision |
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## Technical Specifications |
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#### Hardware |
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We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536. |
