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TheBloke/WizardLM-1.0-Uncensored-Llama2-13B-GGML

TheBloke Llama 13B second-order
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  • classification m-uncensored
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · lifetime
6K
75 last 30d - cooling
Likes
61
Model age
3.2y ago
created 2023-08-06

Training datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

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Metadata

License
llama2
Languages
en
Tags
transformers llama en dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split base_model:QuixiAI/WizardLM-1.0-Uncensored-Llama2-13b base_model:finetune:QuixiAI/WizardLM-1.0-Uncensored-Llama2-13b license:llama2 region:us

Related

Total size
109 GB
Files
20
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2023-09-27 13:01

Files by quantization

Auxiliary files 20 files 109 GB
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q8_0.bin 12.8 GB c5b0f634 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q6_K.bin 9.95 GB 31f2b1ed download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_1.bin 9.10 GB 3ba0d708 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_K_M.bin 8.60 GB b681991b download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_0.bin 8.36 GB b225ccc0 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_K_S.bin 8.36 GB 64e013c8 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_1.bin 7.61 GB fb2043db download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_K_M.bin 7.33 GB b18f5739 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_0.bin 6.86 GB 71933c55 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_K_S.bin 6.86 GB 98b28bb4 download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q3_K_L.bin 6.45 GB 1ea73b4d download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q3_K_M.bin 5.88 GB 78ca2e9a download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q3_K_S.bin 5.27 GB 1886e2ec download
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q2_K.bin 5.13 GB 1178aa17 download
README.md 15.7 KB edd0c629 download
LICENSE.txt 6.86 KB 51089e27 download
USE_POLICY.md 4.65 KB abbcc199 download
.gitattributes 1.48 KB a6344aac download
Notice 112 B d03b5b95 download
config.json 29.0 B a4ba21b7 download

README current version from Hugging Face


language:

  • en
    license: llama2
    datasets:
  • ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
    model_name: WizardLM 1.0 Uncensored Llama2 13B
    inference: false
    model_creator: Eric Hartford
    model_link: https://huggingface.co/ehartford/WizardLM-1.0-Uncensored-Llama2-13b
    model_type: llama
    quantized_by: TheBloke
    base_model: ehartford/WizardLM-1.0-Uncensored-Llama2-13b

TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)


WizardLM 1.0 Uncensored Llama2 13B - GGML

Description

This repo contains GGML format model files for Eric Hartford's WizardLM 1.0 Uncensored Llama2 13B.

Important note regarding GGML files.

The GGML format has now been superseded by GGUF. As of August 21st 2023, llama.cpp no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.

Please use the GGUF models instead.

About GGML

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

  • text-generation-webui, the most popular web UI. Supports NVidia CUDA GPU acceleration.
  • KoboldCpp, a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
  • LM Studio, a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
  • LoLLMS Web UI, a great web UI with CUDA GPU acceleration via the c_transformers backend.
  • ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
  • llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.

Repositories available

Prompt template: WizardLM-Vicuna

You are a helpful AI assistant.

USER: {prompt}
ASSISTANT:

Compatibility

These quantised GGML files are compatible with llama.cpp between June 6th (commit 2d43387) and August 21st 2023.

For support with latest llama.cpp, please use GGUF files instead.

The final llama.cpp commit with support for GGML was: dadbed99e65252d79f81101a392d0d6497b86caa

As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.

Explanation of the new k-quant methods

Click to see details

The new methods available are:

  • GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
  • GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
  • GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
  • GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
  • GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
  • GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name Quant method Bits Size Max RAM required Use case
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q2_K.bin q2_K 2 5.51 GB 8.01 GB New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q3_K_S.bin q3_K_S 3 5.66 GB 8.16 GB New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q3_K_M.bin q3_K_M 3 6.31 GB 8.81 GB New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q3_K_L.bin q3_K_L 3 6.93 GB 9.43 GB New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_0.bin q4_0 4 7.37 GB 9.87 GB Original quant method, 4-bit.
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_K_S.bin q4_K_S 4 7.37 GB 9.87 GB New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_K_M.bin q4_K_M 4 7.87 GB 10.37 GB New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_1.bin q4_1 4 8.17 GB 10.67 GB Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_0.bin q5_0 5 8.97 GB 11.47 GB Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_K_S.bin q5_K_S 5 8.97 GB 11.47 GB New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_K_M.bin q5_K_M 5 9.23 GB 11.73 GB New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_1.bin q5_1 5 9.78 GB 12.28 GB Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q6_K.bin q6_K 6 10.68 GB 13.18 GB New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q8_0.bin q8_0 8 13.79 GB 16.29 GB Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to run in llama.cpp

Make sure you are using llama.cpp from commit dadbed99e65252d79f81101a392d0d6497b86caa or earlier.

For compatibility with latest llama.cpp, please use GGUF files instead.

./main -t 10 -ngl 32 -m wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: Write a story about llamas\nASSISTANT:"

Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 2048 to the desired sequence length for this model. For example, -c 4096 for a Llama 2 model. For models that use RoPE, add --rope-freq-base 10000 --rope-freq-scale 0.5 for doubled context, or --rope-freq-base 10000 --rope-freq-scale 0.25 for 4x context.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text-generation-webui

Further instructions here: text-generation-webui/docs/llama.cpp.md.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Special thanks to: Aemon Algiz.

Patreon special mentions: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Eric Hartford's WizardLM 1.0 Uncensored Llama2 13B

This is a retraining of https://huggingface.co/WizardLM/WizardLM-13B-V1.0 with a filtered dataset, intended to reduce refusals, avoidance, and bias.

Note that LLaMA itself has inherent ethical beliefs, so there's no such thing as a "truly uncensored" model. But this model will be more compliant than WizardLM/WizardLM-13B-V1.0.

Shout out to the open source AI/ML community, and everyone who helped me out.

Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

Like WizardLM/WizardLM-13B-V1.0, this model is trained with Vicuna-1.1 style prompts.

You are a helpful AI assistant.

USER: <prompt>
ASSISTANT:

README history 3 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2023-09-27Update base_model formatting62d8d8e15.7 KB
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  2. 2023-09-05Upload README.mdbc45ad415.6 KB
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  3. 2023-08-06Initial GGML model commite77317213.9 KB
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Discussions 3 threads

  1. 2025-01-31PRAdd text generation tagopen1 💬#3
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  2. 2023-11-29text-generatoropen1 💬#2
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  3. 2023-08-07was unable to load using text-generation-webuiopen8 💬#1
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