Update for Transformers GPTQ support
Browse files- README.md +22 -16
- config.json +35 -24
- gptq_model-4bit-128g.safetensors → model.safetensors +0 -0
- quantize_config.json +1 -1
README.md
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p><a href="https://discord.gg/theblokeai">Chat & support:
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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<!-- header end -->
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# Stablecode Completion Alpha 3B 4K - GPTQ
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| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
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| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
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| [main](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.82 GB | No | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
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| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.96 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
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| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.86 GB | No | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
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| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.82 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
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| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 3.08 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
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| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 3.14 GB | No | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. |
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## How to download from branches
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ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
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<!-- footer start -->
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Special thanks to**:
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**Patreon special mentions**:
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Thank you to all my generous patrons and donaters!
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<!-- footer end -->
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# Original model card: StabilityAI's Stablecode Completion Alpha 3B 4K
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## Model Description
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`StableCode-Completion-Alpha-3B-4K` is a 3 billion parameter decoder-only code completion model pre-trained on diverse set of programming languages that topped the stackoverflow developer survey.
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## Usage
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The model is intended to do single/multiline code completion from a long context window upto 4k tokens.
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### Training Dataset
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The first pre-training stage relies on 300B tokens sourced from various top programming languages occuring in the stackoverflow developer survey present in the `starcoder-data` dataset.
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### Training Procedure
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## How to cite
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```bibtex
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@misc{StableCodeCompleteAlpha4K,
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url={[https://huggingface.co/stabilityai/stablecode-complete-alpha-3b-4k](https://huggingface.co/stabilityai/stablecode-complete-alpha-3b-4k)},
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title={Stable Code Complete Alpha},
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author={Adithyan, Reshinth and Phung, Duy and Cooper, Nathan and Pinnaparaju, Nikhil and Laforte, Christian}
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}
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```
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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<!-- header end -->
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# Stablecode Completion Alpha 3B 4K - GPTQ
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| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
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| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
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| [main](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.82 GB | No | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
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| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.96 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
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| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.86 GB | No | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
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| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 1.82 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
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| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 3.08 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
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| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 3.14 GB | No | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. |
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## How to download from branches
|
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ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
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<!-- footer start -->
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<!-- 200823 -->
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Special thanks to**: Aemon Algiz.
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**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
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Thank you to all my generous patrons and donaters!
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And thank you again to a16z for their generous grant.
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<!-- footer end -->
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# Original model card: StabilityAI's Stablecode Completion Alpha 3B 4K
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## Model Description
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`StableCode-Completion-Alpha-3B-4K` is a 3 billion parameter decoder-only code completion model pre-trained on diverse set of programming languages that topped the stackoverflow developer survey.
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## Usage
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The model is intended to do single/multiline code completion from a long context window upto 4k tokens.
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### Training Dataset
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The first pre-training stage relies on 300B tokens sourced from various top programming languages occuring in the stackoverflow developer survey present in the `starcoder-data` dataset.
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### Training Procedure
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## How to cite
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```bibtex
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@misc{StableCodeCompleteAlpha4K,
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url={[https://huggingface.co/stabilityai/stablecode-complete-alpha-3b-4k](https://huggingface.co/stabilityai/stablecode-complete-alpha-3b-4k)},
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title={Stable Code Complete Alpha},
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author={Adithyan, Reshinth and Phung, Duy and Cooper, Nathan and Pinnaparaju, Nikhil and Laforte, Christian}
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}
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```
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config.json
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}
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{
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"architectures": [
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"GPTNeoXForCausalLM"
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],
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"bos_token_id": 0,
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"classifier_dropout": 0.1,
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"eos_token_id": 0,
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"hidden_act": "gelu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 10240,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 4096,
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"model_type": "gpt_neox",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.30.2",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 49152,
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"pretraining_tp": 1,
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"quantization_config": {
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"bits": 4,
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"group_size": 128,
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"damp_percent": 0.1,
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"desc_act": true,
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"sym": true,
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"true_sequential": true,
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"model_name_or_path": null,
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"model_file_base_name": "model",
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"quant_method": "gptq"
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}
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}
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gptq_model-4bit-128g.safetensors → model.safetensors
RENAMED
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quantize_config.json
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"sym": true,
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"true_sequential": true,
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"model_file_base_name":
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}
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"sym": true,
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"true_sequential": true,
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"model_file_base_name": "model"
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}
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