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Browse files- .gitattributes +10 -0
- README.md +63 -0
- Sentdex-WSB-GPT-13B.Q2_K.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q3_K_L.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q3_K_M.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q3_K_S.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q4_K_M.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q4_K_S.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q5_K_M.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q5_K_S.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q6_K.gguf +3 -0
- Sentdex-WSB-GPT-13B.Q8_0.gguf +3 -0
.gitattributes
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@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: openrail
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pipeline_tag: text-generation
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library_name: transformers
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language:
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- zh
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- en
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---
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## Original model card
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Buy me a coffee if you like this project ;)
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<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
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#### Description
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GGUF Format model files for [This project](https://huggingface.co/Sentdex/WSB-GPT-13B).
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### GGUF Specs
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GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
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Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
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Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
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mmap compatibility: models can be loaded using mmap for fast loading and saving.
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Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
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Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
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The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
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This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
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inference or for identifying the model.
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### Perplexity params
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Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16
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7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066
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13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543
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### inference
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```python
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import ctransformers
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from ctransformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(output_dir, gguf_file,
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gpu_layers=32, model_type="llama")
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manual_input: str = "Tell me about your last dream, please."
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llm(manual_input,
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max_new_tokens=256,
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temperature=0.9,
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top_p= 0.7)
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```
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# Original model card
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Sentdex-WSB-GPT-13B.Q2_K.gguf
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