Readme Init

README.md

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 ---
 license: apache-2.0
+inference: true
+tags:
+- vicuna
 ---
+![demo](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_08.png)
+
+**This model is a 8bit quantization of Vicuna 13B.**  
+- 13B parameters
+- Group size: 128
+- wbits: 8
+- true-sequential: yes
+- act-order: yes
+- 8-bit quantized - Read more about this here: https://github.com/ggerganov/llama.cpp/pull/951
+- Conversion process: Llama 13B -> Llama 13B HF -> Vicuna13B-v1.1 HF -> Vicuna13B-v1.1-8bit-128g
+
+<br>
+<br>
+
+# Basic installation procedure
+
+- It was a nightmare, I will only detail briefly what you'll need. WSL was quite painful to sort out.
+- I will not provide installation support, sorry.
+- You can certainly use llama.cpp and other loaders that support 8bit quantization, I just chose oobabooga/text-generation-webui.
+- You will likely face many bugs until text-generation-webui loads, ranging between missing PATH or env variables to having to manually pip uninstall/install packages.
+- The notes below will likely become outdated once both text-generation-webui and GPTQ-for-LLaMa receive the appropriate bug fixes.
+- If this model produces very slow answers (1 token/s), it means you are not using Cuda for bitsandbytes or that your hardware needs an upgrade.
+- If this model produces answers with weird characters, it means you are not using the correct version of qwopqwop200/GPTQ-for-LLaMa as mentioned below.
+- If this model produces answers that are out of topic or if it talks to itself, it means you are not using the correct checkout 508de42 of qwopqwop200/GPTQ-for-LLaMa as mentioned below.
+
+Cuda (Slow tokens/s):
+```
+git clone https://github.com/oobabooga/text-generation-webui
+cd text-generation-webui
+pip install -r requirements.txt
+
+mkdir repositories
+cd repositories
+git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda # Make sure you obtain the qwopqwop200 version, not the oobabooga one! (because "act-order: yes")
+cd GPTQ-for-LLaMa
+pip install -r requirements.txt
+python setup_cuda.py install
+```
+
+Triton (Fast tokens/s) - Works on Windows with WSL (what I've used) or Linux:
+```
+git clone https://github.com/oobabooga/text-generation-webui
+cd text-generation-webui
+git fetch origin pull/1229/head:triton # This is the version that supports Triton - https://github.com/oobabooga/text-generation-webui/pull/1229
+git checkout triton
+pip install -r requirements.txt
+
+mkdir repositories
+cd repositories
+git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git # -b cuda
+cd GPTQ-for-LLaMa
+git checkout 508de42 # Before qwopqwop200 broke everything... - https://github.com/qwopqwop200/GPTQ-for-LLaMa/issues/183
+pip install -r requirements.txt
+```
+
+<br>
+<br>
+
+# Testbench detail and results
+
+- Latest version of oobabooga + https://github.com/oobabooga/text-generation-webui/pull/1229
+
+- NVIDIA GTX 3090
+- 32BG DDR4
+- i9-7980XE OC @4.6Ghz
+
+- 11 tokens/s on average with Triton
+- Preliminary observations: better results than --load-in-8bits (To Be Confirmed)
+- Tested and working in both chat mode and text generation mode
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_01.png)
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_02.png)
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_03.png)
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_04.png)
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_05.png)
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_06.png)
+
+![screenshot](https://thireus.com/AI/Thireus_Vicuna13B-v1.1-8bit-128g_07.png)
+
+<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.