thanks to TheBloke ❤

README.md

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+---
+license: other
+inference: false
+---
+# Vicuna 13B 1.1 GPTQ 4bit 128g
+
+This is a 4-bit GPTQ version of the [Vicuna 13B 1.1 model](https://huggingface.co/lmsys/vicuna-13b-delta-v1.1).
+
+It was created by merging the deltas provided in the above repo with the original Llama 13B model, [using the code provided on their Github page](https://github.com/lm-sys/FastChat#vicuna-weights).
+
+It was then quantized to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+
+## Want to try this in Colab for free?
+
+Check out this Google Colab provided by [eucdee](https://huggingface.co/eucdee): [Google Colab for Vicuna 1.1](https://colab.research.google.com/github/eucdee/AI/blob/main/4bit_TextGen_Gdrive.ipynb)
+
+## My Vicuna 1.1 model repositories
+
+I have the following Vicuna 1.1 repositories available:
+
+**13B models:**
+* [Unquantized 13B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-13B-1.1-HF)
+* [GPTQ quantized 4bit 13B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g)
+* [GPTQ quantized 4bit 13B 1.1 for CPU - GGML format for `llama.cpp`](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML)
+  
+**7B models:**
+* [Unquantized 7B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-7B-1.1-HF)
+* [GPTQ quantized 4bit 7B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-7B-1.1-GPTQ-4bit-128g)
+* [GPTQ quantized 4bit 7B 1.1 for CPU - GGML format for `llama.cpp`](https://huggingface.co/TheBloke/vicuna-7B-1.1-GPTQ-4bit-128g-GGML)
+
+## GIBBERISH OUTPUT
+
+If you get gibberish output, it is because you are using the `safetensors` file without updating GPTQ-for-LLaMA.
+
+If you use the `safetensors` file you must have the latest version of GPTQ-for-LLaMA inside text-generation-webui.
+
+If you don't want to update, or you can't, use the `pt` file instead.
+
+Either way, please read the instructions below carefully.
+
+## Provided files
+
+Two model files are provided. Ideally use the `safetensors` file. Full details below:
+
+Details of the files provided:
+* `vicuna-13B-1.1-GPTQ-4bit-128g.safetensors`
+  * `safetensors` format, with improved file security, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
+  * Command to create:
+    * `python3 llama.py vicuna-13B-1.1-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors vicuna-13B-1.1-GPTQ-4bit-128g.safetensors`
+* `vicuna-13B-1.1-GPTQ-4bit-128g.no-act-order.pt`
+  * `pt` format file, created without the `--act-order` flag.
+  * This file may have slightly lower quality, but is included as it can be used without needing to compile the latest GPTQ-for-LLaMa code.
+  * It should hopefully therefore work with one-click-installers on Windows, which include the older GPTQ-for-LLaMa code.
+  * Command to create:
+    * `python3 llama.py vicuna-13B-1.1-HF c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors vicuna-13B-1.1-GPTQ-4bit-128g.no-act-order.pt`
+
+## How to run in `text-generation-webui`
+
+File `vicuna-13B-1.1-GPTQ-4bit-128g.no-act-order.pt` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+The `safetensors` model file was created with the latest GPTQ code, and uses `--act-order` to give the maximum possible quantisation quality, but this means it requires that the latest GPTQ-for-LLaMa is used inside the UI.
+
+If you want to use the `safetensors` file and need to update GPTQ-for-LLaMa, here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
+```
+# We need to clone GPTQ-for-LLaMa as of April 13th, due to breaking changes in more recent commits
+git clone -n  https://github.com/qwopqwop200/GPTQ-for-LLaMa gptq-safe
+cd gptq-safe && git checkout 58c8ab4c7aaccc50f507fd08cce941976affe5e0
+
+# Now clone text-generation-webui, if you don't already have it
+git clone https://github.com/oobabooga/text-generation-webui
+# And link GPTQ-for-Llama into text-generation-webui
+mkdir -p text-generation-webui/repositories
+ln -s gptq-safe text-generation-webui/repositories/GPTQ-for-LLaMa
+```
+
+Then install this model into `text-generation-webui/models` and launch the UI as follows:
+```
+cd text-generation-webui
+python server.py --model vicuna-13B-1.1-GPTQ-4bit-128g --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
+```
+
+The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.
+
+If you are on Windows, or cannot use the Triton branch of GPTQ for any other reason, you can instead use the CUDA branch:
+```
+git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda
+cd GPTQ-for-LLaMa
+python setup_cuda.py install
+```
+Then link that into `text-generation-webui/repositories` as described above.
+
+Or just use `vicuna-13B-1.1-GPTQ-4bit-128g.no-act-order.pt` as mentioned above, which should work without any upgrades to text-generation-webui.
+
+# 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.

config.json

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+{
+  "_name_or_path": "/content/llama-13b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 2048,
+  "max_sequence_length": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": -1,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.0.dev0"
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+size 499723

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

vicuna-13B-1.1-GPTQ-4bit-128g.no-act-order.pt

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+size 7255476788

vicuna-13B-1.1-GPTQ-4bit-128g.safetensors

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