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--- |
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language: |
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- en |
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library_name: transformers |
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extra_gated_prompt: >- |
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This model is exclusively available to Pro subscribers of [The |
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Kaitchup](https://newsletter.kaitchup.com/). To gain access, subscribe to The |
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Kaitchup Pro, [subscribe here](https://newsletter.kaitchup.com/subscribe). If |
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you are already a Pro subscriber, you will find your access token at the |
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bottom of this |
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[page](https://newsletter.kaitchup.com/p/introducing-minivoc-faster-and-memory-llms). |
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datasets: |
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- HuggingFaceFW/fineweb-edu |
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--- |
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## Model Details |
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This is [kaitchup/Qwen2.5-7B-Minivoc-32k-v0.1a](https://huggingface.co/kaitchup/Qwen2.5-7B-Minivoc-32k-v0.1a) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main) (asymmetric quantization) and serialized with the GPTQ format in 4-bit. The model has been created, tested, and evaluated by The Kaitchup. |
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The Minivoc approach reduces the vocabulary size to save memory during inference and fine-tuning. |
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Details on the quantization process and how to use the model here: |
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[The Best Quantization Methods to Run Llama 3.1 on Your GPU](https://newsletter.kaitchup.com/p/the-best-quantization-methods-to) |
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It is possible to fine-tune an adapter on top of it following the QLoRA methodology. More about this here: |
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[QLoRA with AutoRound: Cheaper and Better LLM Fine-tuning on Your GPU](https://newsletter.kaitchup.com/p/qlora-with-autoround-cheaper-and) |
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I used these hyperparameters for quantization: |
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``` |
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bits, group_size = 4, 128 |
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autoround = AutoRound(model, tokenizer, nsamples=512, iters=1000, low_gpu_mem_usage=False, bits=bits, group_size=group_size) |
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autoround.quantize() |
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output_dir = "./tmp_autoround" |
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autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) |
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``` |
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Evaluation results (zero-shot evaluation with lm_eval): |
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- **Developed by:** [The Kaitchup](https://newsletter.kaitchup.com/) |
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- **Language(s) (NLP):** English |
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- **License:** Contact me |
