NeuralNovel
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README.md
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---
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A simple converter which converts pytorch .bin tensor files (Usually listed as "pytorch_model.bin" or "pytorch_model-xxxx-of-xxxx.bin") to safetensor files. Reason?
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~~because it's cool!~~
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Because the safetensor format decreases the loading time of large LLM models, currently supported in [oobabooga's text-generation-webui](https://github.com/oobabooga/text-generation-webui). It also supports in-place loading, which effectively decreased the required memory to load a LLM.
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Note: Most of the code originated from [Convert to Safetensors - a Hugging Face Space by safetensors](https://huggingface.co/spaces/safetensors/convert), and this code cannot deal with files that are not named as "pytorch_model.bin" or "pytorch_model-xxxx-of-xxxx.bin".
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### Limitations:
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The program requires **A lot** of memory. To be specific, your idle memory should be **at least** twice the size of your largest ".bin" file. Or else, the program will run out of memory and use your swap... that would be **slow!**
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This program **will not** re-shard (aka break down) the model, you'll need to do it yourself using some other tools.
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### Usage:
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git clone https://github.com/Silver267/pytorch-to-safetensor-converter.git
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cd pytorch-to-safetensor-converter
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pip install -r requirements.txt
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```
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```
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python convert_to_safetensor.py
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```
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Follow the instruction in the program. Remember to use the **full path** for the model directory (Something like ``E:\models\xxx-fp16`` that contains all the model files). Wait for a while, and you're good to go. The program will automatically copy all other files to your destination folder, enjoy!
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if your original model is fp32 then don't forget to edit ``"torch_dtype": "float32",`` to ``"torch_dtype": "float16",`` in ``config.json``
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#### Note that this operation might (in rare occasions) cause the LLM to output NaN while performing operations since it decreases the precision to fp16.
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If you're worried about that, simply edit the line ``loaded = {k: v.contiguous().half() for k, v in loaded.items()}`` in ``convert_to_safetensor.py`` into ``loaded = {k: v.contiguous() for k, v in loaded.items()}`` and you'll have a full precision model.
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---
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license: other
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license_name: yi-license
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license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
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base_model: jondurbin/bagel-34b-v0.2
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---
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![image/png](https://i.ibb.co/9VB5SHL/OIG1-3.jpg)
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# ConvexAI/Luminex-34B-v0.1
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This model is [Smaug-34b](https://huggingface.co/abacusai/Smaug-34B-v0.1) with LaserRMT applied.
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### Evaluation Results
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Coming Soon
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### Contamination Results
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Coming Soon
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