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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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tags: |
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- alpaca |
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- llama |
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- chat |
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- gpt4 |
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inference: false |
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--- |
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# GPT4 Alpaca LoRA 30B - 4bit GGML |
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This is a 4-bit GGML version of the [Chansung GPT4 Alpaca 30B LoRA model](https://huggingface.co/chansung/gpt4-alpaca-lora-30b). |
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It was created by merging the LoRA provided in the above repo with the original Llama 30B model, producing unquantised model [GPT4-Alpaca-LoRA-30B-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF) |
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The files in this repo were then quantized to 4bit and 5bit for use with [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)! |
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llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508 |
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I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them. |
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For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`. |
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## Provided files |
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| Name | Quant method | Bits | Size | RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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`gpt4-alpaca-lora-30B.ggmlv3.q4_0.bin` | q4_0 | 4bit | 20.3GB | 23GB | 4bit. | |
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`gpt4-alpaca-lora-30B.ggmlv3.q4_1.bin` | q4_1 | 4bit | 22.4GB | 25GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | |
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`gpt4-alpaca-lora-30B.ggmlv3.q5_0.bin` | q5_0 | 5bit | 22.4GB | 25GB | 5bit. Higher accuracy, higher resource usage, slower inference. | |
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`gpt4-alpaca-lora-30B.ggmlv3.q5_1.bin` | q5_1 | 5bit | 24.4GB | 27GB | 5bit. Even higher accuracy and resource usage, and slower inference. | |
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## How to run in `llama.cpp` |
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I use the following command line; adjust for your tastes and needs: |
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``` |
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./main -t 18 -m gpt4-alpaca-lora-30B.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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Write a story about llamas |
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### Response:" |
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``` |
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Change `-t 18` to the number of physical CPU cores you have. For example if your system has 6 cores/12 threads, use `-t 6`. |
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If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` |
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## How to run in `text-generation-webui` |
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Create a model directory that has `ggml` (case sensitive) in its name. Then put the desired .bin file in that model directory. |
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Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). |
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Note: at this time text-generation-webui may not support the new May 19th llama.cpp quantisation methods for q4_0, q4_1 and q8_0 files. |
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# Original GPT4 Alpaca Lora model card |
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This repository comes with LoRA checkpoint to make LLaMA into a chatbot like language model. The checkpoint is the output of instruction following fine-tuning process with the following settings on 8xA100(40G) DGX system. |
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- Training script: borrowed from the official [Alpaca-LoRA](https://github.com/tloen/alpaca-lora) implementation |
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- Training script: |
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```shell |
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python finetune.py \ |
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--base_model='decapoda-research/llama-30b-hf' \ |
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--data_path='alpaca_data_gpt4.json' \ |
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--num_epochs=10 \ |
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--cutoff_len=512 \ |
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--group_by_length \ |
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--output_dir='./gpt4-alpaca-lora-30b' \ |
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--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \ |
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--lora_r=16 \ |
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--batch_size=... \ |
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--micro_batch_size=... |
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``` |
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You can find how the training went from W&B report [here](https://wandb.ai/chansung18/gpt4_alpaca_lora/runs/w3syd157?workspace=user-chansung18). |
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