Vicuna 30B GGML

From LMSYS: https://huggingface.co/lmsys/vicuna-33b-v1.3

Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`

Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48.

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`

Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387.

Files

Name	Quant method	Bits	Size	Max RAM required, no GPU offloading	Use case
vicuna-33b.ggmlv3.q2_K.bin	q2_K	2	13.71 GB	16.21 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-33b.ggmlv3.q3_K_L.bin	q3_K_L	3	17.28 GB	19.78 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-33b.ggmlv3.q3_K_M.bin	q3_K_M	3	15.72 GB	18.22 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-33b.ggmlv3.q3_K_S.bin	q3_K_S	3	14.06 GB	16.56 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
vicuna-33b.ggmlv3.q4_0.bin	q4_0	4	18.30 GB	20.80 GB	Original llama.cpp quant method, 4-bit.
vicuna-33b.ggmlv3.q4_1.bin	q4_1	4	20.33 GB	22.83 GB	Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
vicuna-33b.ggmlv3.q4_K_M.bin	q4_K_M	4	19.62 GB	22.12 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-33b.ggmlv3.q4_K_S.bin	q4_K_S	4	18.36 GB	20.86 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
vicuna-33b.ggmlv3.q5_0.bin	q5_0	5	22.37 GB	24.87 GB	Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
vicuna-33b.ggmlv3.q5_1.bin	q5_1	5	24.40 GB	26.90 GB	Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
vicuna-33b.ggmlv3.q5_K_M.bin	q5_K_M	5	23.05 GB	25.55 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-33b.ggmlv3.q5_K_S.bin	q5_K_S	5	22.40 GB	24.90 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
vicuna-33b.ggmlv3.q6_K.bin	q6_K	6	26.69 GB	29.19 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
vicuna-33b.ggmlv3.q8_0.bin	q8_0	8	34.56 GB	37.06 GB	Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Vicuna Model Card

Model Details

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

Developed by: LMSYS
Model type: An auto-regressive language model based on the transformer architecture.
License: Non-commercial license
Finetuned from model: LLaMA.

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 v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this paper.

Evaluation

Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this paper.

Difference between different versions of Vicuna

See vicuna_weights_version.md