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---
inference: false
license: other
model_creator: lmsys
model_link: https://huggingface.co/lmsys/vicuna-13b-v1.5-16k
model_name: Vicuna 13B v1.5 16K
model_type: llama
quantized_by: TheBloke
---

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# Vicuna 13B v1.5 16K - GGML
- Model creator: [lmsys](https://huggingface.co/lmsys)
- Original model: [Vicuna 13B v1.5 16K](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k)

## Description

This repo contains GGML format model files for [lmsys's Vicuna 13B v1.5 16K](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k).

GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with CUDA GPU acceleration via the c_transformers backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.

## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML)
* [lmsys's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k)

## 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_ggml start -->
## Compatibility

### 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
<details>
  <summary>Click to see details</summary>

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.
</details>
<!-- compatibility_ggml end -->

## Provided files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [vicuna-13b-v1.5-16k.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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. |
| [vicuna-13b-v1.5-16k.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.37 GB| 9.87 GB | Original quant method, 4-bit. |
| [vicuna-13b-v1.5-16k.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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. |
| [vicuna-13b-v1.5-16k.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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. |
| [vicuna-13b-v1.5-16k.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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. |
| [vicuna-13b-v1.5-16k.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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 |
| [vicuna-13b-v1.5-16k.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML/blob/main/vicuna-13b-v1.5-16k.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`

I use the following command line; adjust for your tastes and needs:

```
./main -t 10 -ngl 32 -m vicuna-13b-v1.5-16k.ggmlv3.q4_K_M.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](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).

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Thanks to the [chirper.ai](https://chirper.ai) team!

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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.

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# Original model card: lmsys's Vicuna 13B v1.5 16K


**Note:** This is a preview version. A slightly better checkpoint will be uploaded soon.

# Vicuna Model Card

## Model Details

Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.

- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement	
- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)

### Model Sources

- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/

## Uses

The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

## How to Get Started with the Model

- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api  

## Training Details

Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling.
The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).

## Evaluation

![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)

Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).

## Difference between different versions of Vicuna

See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)