inference: false
license: other
WizardLM's WizardLM 13B V1.1 GGML
These files are GGML format model files for WizardLM's WizardLM 13B V1.1.
GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:
Update 9th July 2023: GGML k-quants now available
Thanks to the work of LostRuins/concedo, it is now possible to provide 100% working GGML k-quants for models like this which have a non-standard vocab size (32,001).
k-quants have been uploaded and will work with all llama.cpp clients without any changes required.
Repositories available
- 4-bit GPTQ models for GPU inference
- 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
- Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Vicuna
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: PROMPT
ASSISTANT:
Compatibility
Note: due to this model having a non-standard vocab size of 32,001, k-quants are slightly larger than they are for other models of the same size and type.
For example, a 13B q4_K_M will be around 150MB larger. Inference speed should not be noticeably affected, and quality will be the same or higher.
Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0
These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.
New k-quant methods: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K
These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
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-13b-v1.1.ggmlv3.q2_K.bin | q2_K | 2 | 5.67 GB | 8.17 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-13b-v1.1.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 7.07 GB | 9.57 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-13b-v1.1.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.46 GB | 8.96 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-13b-v1.1.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.80 GB | 8.30 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| wizardlm-13b-v1.1.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original quant method, 4-bit. |
| wizardlm-13b-v1.1.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 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-13b-v1.1.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.99 GB | 10.49 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-13b-v1.1.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.49 GB | 9.99 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| wizardlm-13b-v1.1.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| wizardlm-13b-v1.1.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| wizardlm-13b-v1.1.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.33 GB | 11.83 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-13b-v1.1.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 9.07 GB | 11.57 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| wizardlm-13b-v1.1.ggmlv3.q6_K.bin | q6_K | 6 | 10.76 GB | 13.26 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| wizardlm-13b-v1.1.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 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
I use the following command line; adjust for your tastes and needs:
./main -t 10 -ngl 32 -m wizardlm-13b-v1.1.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
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.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to run in text-generation-webui
Further instructions here: text-generation-webui/docs/llama.cpp-models.md.
Discord
For further support, and discussions on these models and AI in general, join us at:
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.
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Special thanks to: Luke from CarbonQuill, Aemon Algiz.
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Thank you to all my generous patrons and donaters!
Original model card: WizardLM's WizardLM 13B V1.1
This is the Full-Weight of WizardLM-13B V1.1 model.
Repository: https://github.com/nlpxucan/WizardLM
Twitter: https://twitter.com/WizardLM_AI/status/1677282955490918401
- 🔥🔥🔥 [7/7/2023] We released WizardLM V1.1 models. The WizardLM-13B-V1.1 is here (Demo_13B-V1.1, Demo_13B-V1.1_bak-1, Demo_13B-V1.1_bak-2, Full Model Weight). WizardLM-7B-V1.1, WizardLM-30B-V1.1, and WizardLM-65B-V1.1 are coming soon. Please checkout the Full Model Weights and paper.
- 🔥🔥🔥 [7/7/2023] The WizardLM-13B-V1.1 achieves 6.74 on MT-Bench Leaderboard, 86.32% on AlpacaEval Leaderboard, and 99.3% on WizardLM Eval. (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)