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+wizard-vicuna-13B-GPTQ-8bit-128g.no-act-order.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+  - en
+tags:
+  - causal-lm
+  - llama
+inference: false
+---
+
+# Wizard-Vicuna-13B-GPTQ-8bit-128g
+
+This repository contains 8-bit quantized models in GPTQ format of [TheBlokes's wizard-vicuna 13B in FP16 HF format](https://huggingface.co/TheBloke/wizard-vicuna-13B-HF).
+
+These models are the result of quantization to 8-bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+
+While most metrics suggest that 8-bit is only marginally better than 4-bit, I have found that the 8-bit model follows instructions significantly better. The responses from the 8-bit model feel very close to the quality of GPT-3, whereas the 4-bit model lacks some "intelligence".
+
+With this quantized model, I can replace GPT-3 for most of my work. However, a drawback is that it requires approximately 15GB of VRAM, so you need a GPU with at least 16GB of VRAM.
+
+
+The content below is straight copy and paste from TheBloke's README with the 4 bit content changed to 8 bit and referencing this model.
+
+ 
+## How to easily download and use this model in text-generation-webui
+
+Open the text-generation-webui UI as normal.
+
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `deetungsten/wizard-vicuna-13B-GPTQ-8bit-128g`.
+3. Click **Download**.
+4. Wait until it says it's finished downloading.
+5. Click the **Refresh** icon next to **Model** in the top left.
+6. In the **Model drop-down**: choose the model you just downloaded, `wizard-vicuna-13B-GPTQ-8bit-128g`.
+7. If you see an error in the bottom right, ignore it - it's temporary.
+8. Fill out the `GPTQ parameters` on the right: `Bits = 8`, `Groupsize = 128`, `model_type = Llama`
+9. Click **Save settings for this model** in the top right.
+10. Click **Reload the Model** in the top right.
+11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
+
+## Provided files
+
+**Compatible file - wizard-vicuna-13B-GPTQ-8bit-128g.no-act-order.safetensors**
+
+In the `main` branch - the default one - you will find `wizard-vicuna-13B-GPTQ-8bit-128g.no-act-order.safetensors`
+
+This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility
+
+It was created without the `--act-order` parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui.
+
+* `wizard-vicuna-13B-GPTQ-8bit-128g.no-act-order.safetensors`
+  * Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
+  * Works with text-generation-webui one-click-installers
+  * Parameters: Groupsize = 128g. No act-order.
+  * Command used to create the GPTQ:
+    ```
+    CUDA_VISIBLE_DEVICES=0 python3 llama.py wizard-vicuna-13B-HF c4 --wbits 8 --true-sequential --groupsize 128 --save_safetensors wizard-vicuna-13B-GPTQ-8bit.compat.no-act-order.safetensors
+    ```
+   
+# Original WizardVicuna-13B model card
+
+Github page: https://github.com/melodysdreamj/WizardVicunaLM
+
+# WizardVicunaLM
+### Wizard's dataset + ChatGPT's conversation extension + Vicuna's tuning method
+I am a big fan of the ideas behind WizardLM and VicunaLM. I particularly like the idea of WizardLM handling the dataset itself more deeply and broadly, as well as VicunaLM overcoming the limitations of single-turn conversations by introducing multi-round conversations. As a result, I combined these two ideas to create WizardVicunaLM. This project is highly experimental and designed for proof of concept, not for actual usage.
+
+
+## Benchmark
+### Approximately 7% performance improvement over VicunaLM
+![](https://user-images.githubusercontent.com/21379657/236088663-3fa212c9-0112-4d44-9b01-f16ea093cb67.png)
+
+
+### Detail 
+
+The questions presented here are not from rigorous tests, but rather, I asked a few questions and requested GPT-4 to score them. The models compared were ChatGPT 3.5, WizardVicunaLM, VicunaLM, and WizardLM, in that order.
+
+|     | gpt3.5 | wizard-vicuna-13b | vicuna-13b | wizard-7b | link     |
+|-----|--------|-------------------|------------|-----------|----------|
+| Q1  | 95     | 90                | 85         | 88        | [link](https://sharegpt.com/c/YdhIlby) |
+| Q2  | 95     | 97                | 90         | 89        | [link](https://sharegpt.com/c/YOqOV4g) |
+| Q3  | 85     | 90                | 80         | 65        | [link](https://sharegpt.com/c/uDmrcL9) |
+| Q4  | 90     | 85                | 80         | 75        | [link](https://sharegpt.com/c/XBbK5MZ) |
+| Q5  | 90     | 85                | 80         | 75        | [link](https://sharegpt.com/c/AQ5tgQX) |
+| Q6  | 92     | 85                | 87         | 88        | [link](https://sharegpt.com/c/eVYwfIr) |
+| Q7  | 95     | 90                | 85         | 92        | [link](https://sharegpt.com/c/Kqyeub4) |
+| Q8  | 90     | 85                | 75         | 70        | [link](https://sharegpt.com/c/M0gIjMF) |
+| Q9  | 92     | 85                | 70         | 60        | [link](https://sharegpt.com/c/fOvMtQt) |
+| Q10 | 90     | 80                | 75         | 85        | [link](https://sharegpt.com/c/YYiCaUz) |
+| Q11 | 90     | 85                | 75         | 65        | [link](https://sharegpt.com/c/HMkKKGU) |
+| Q12 | 85     | 90                | 80         | 88        | [link](https://sharegpt.com/c/XbW6jgB) |
+| Q13 | 90     | 95                | 88         | 85        | [link](https://sharegpt.com/c/JXZb7y6) |
+| Q14 | 94     | 89                | 90         | 91        | [link](https://sharegpt.com/c/cTXH4IS) |
+| Q15 | 90     | 85                | 88         | 87        | [link](https://sharegpt.com/c/GZiM0Yt) |
+|     | 91     | 88                | 82         | 80        |          |
+
+
+## Principle
+
+We adopted the approach of WizardLM, which is to extend a single problem more in-depth. However, instead of using individual instructions, we expanded it using Vicuna's conversation format and applied Vicuna's fine-tuning techniques.
+
+Turning a single command into a rich conversation is what we've done [here](https://sharegpt.com/c/6cmxqq0).
+
+After creating the training data, I later trained it according to the Vicuna v1.1 [training method](https://github.com/lm-sys/FastChat/blob/main/scripts/train_vicuna_13b.sh).
+
+
+## Detailed Method
+
+First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. However, we made it in a continuous conversation format instead of the instruction format. That is, it starts with WizardLM's instruction, and then expands into various areas in one conversation using ChatGPT 3.5.
+
+After that, we applied the following model using Vicuna's fine-tuning format.
+
+## Training Process
+
+Trained with 8 A100 GPUs for 35 hours.
+
+## Weights
+You can see the [dataset](https://huggingface.co/datasets/junelee/wizard_vicuna_70k) we used for training and the [13b model](https://huggingface.co/junelee/wizard-vicuna-13b) in the huggingface.
+
+## Conclusion
+If we extend the conversation to gpt4 32K, we can expect a dramatic improvement, as we can generate 8x more, more accurate and richer conversations.
+
+## License
+The model is licensed under the LLaMA model, and the dataset is licensed under the terms of OpenAI because it uses ChatGPT. Everything else is free.
+
+## Author
+
+[JUNE LEE](https://github.com/melodysdreamj) - He is active in Songdo Artificial Intelligence Study and GDG Songdo.

config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "wizard_vicuna_13b_600_step",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.1"
+}

huggingface-metadata.txt

@@ -0,0 +1,6 @@
+url: https://huggingface.co/TheBloke/wizard-vicuna-13B-GPTQ
+branch: main
+download date: 2023-05-11 17:51:27
+sha256sum:
+    9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 tokenizer.model
+    f73b241fd29129c0f6f6024719fd22eeb1d0cac0dceba2c7151bd70b5e654640 wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors

quantize_config.json

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+{
+  "bits": 4,
+  "desc_act": false,
+  "group_size": 128
+}

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
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+  }
+}

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tokenizer_config.json

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+  "add_eos_token": false,
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+    "__type": "AddedToken",
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+  "padding_side": "right",
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+}

wizard-vicuna-13B-GPTQ-8bit-128g.no-act-order.safetensors

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