File size: 2,456 Bytes
ae12e3a ad7c2a0 ae12e3a 8e3f264 ae12e3a bb35084 f2c2e2d f701348 f2c2e2d ae12e3a 0f22b96 ae12e3a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
license: apache-2.0
inference: false
---
**NOTE: This GGML conversion is primarily for use with llama.cpp.**
- 13B parameters
- 4-bit quantized
- Based on version 1.1
- Used PR "More accurate Q4_0 and Q4_1 quantizations #896" (should be closer in quality to unquantized)
- For q4_2, "Q4_2 ARM #1046" was used. Will update regularly if new changes are made.
- **Choosing between q4_0, q4_1, and q4_2:**
- 4_0 is the fastest. The quality is the poorest.
- 4_1 is a lot slower. The quality is noticeably better.
- 4_2 is almost as fast as 4_0 and about as good as 4_1 **on Apple Silicon**. On Intel/AMD it's hardly better or faster than 4_1.
- 7B version of this can be found here: https://huggingface.co/eachadea/ggml-vicuna-7b-1.1
<br>
<br>
# Vicuna Model Card
## Model details
**Model type:**
Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
Vicuna was trained between March 2023 and April 2023.
**Organizations developing the model:**
The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
**Paper or resources for more information:**
https://vicuna.lmsys.org/
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/lm-sys/FastChat/issues
## Intended use
**Primary intended uses:**
The primary use of Vicuna is research on large language models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## Training dataset
70K conversations collected from ShareGPT.com.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
## Major updates of weights v1.1
- Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
- Fix the supervised fine-tuning loss computation for better model quality. |