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--- |
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license: other |
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inference: false |
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--- |
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# OpenAssistant LLaMA 30B SFT 7 GPTQ |
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This in a repo of GPTQ format 4bit quantised models for [OpenAssistant's LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor). |
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It is the result of merging the XORs from the above repo with the original Llama 30B weights, and then quantising to 4bit GPU inference using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). |
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This is epoch 7 of OpenAssistant's training of their Llama 30B model. |
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**Please note that these models will need 24GB VRAM or greater to use effectively** |
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## Repositories available |
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* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ). |
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* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GGML). |
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* [Unquantised 16bit model in HF format](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-HF). |
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## PROMPT TEMPLATE |
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This model requires the following prompt template: |
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``` |
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<|prompter|> prompt goes here |
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<|assistant|>: |
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``` |
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## CHOICE OF MODELS |
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Two sets of models are provided: |
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* Groupsize = 1024 |
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* Should work reliably in 24GB VRAM |
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* Groupsize = 128 |
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* Optimal setting for highest inference quality |
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* But may require more than 24GB VRAM, depending on response length |
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* In my testing it ran out of VRAM on a 24GB card around 1500 tokens returned. |
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For each model, two versions are available: |
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* `compat.no-act-order.safetensor` |
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* Works with all versions of GPTQ-for-LLaMa, including the version in text-generation-webui one-click-installers |
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* `latest.act-order.safetensors` |
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* uses `--act-order` for higher inference quality |
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* requires more recent GPTQ-for-LLaMa code, therefore will not currently work with one-click-installers |
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## HOW TO CHOOSE YOUR MODEL |
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I have used branches to separate the models. This means you can clone the branch you want and not got model files you don't need. |
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* Branch: **main** = groupsize 1024, `compat.no-act-order.safetensor` file |
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* Branch: **1024-latest** = groupsize 1024, `latest.no-act-order.safetensor` file |
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* Branch: **128-compat** = groupsize 128, `compat.no-act-order.safetensor` file |
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* Branch: **128-latest** = groupsize 128, `latest.no-act-order.safetensor` file |
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## How to easily download and run the 1024g compat model in text-generation-webui |
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Open the text-generation-webui UI as normal. |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ`. |
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3. Click **Download**. |
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4. Wait until it says it's finished downloading. |
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5. Click the **Refresh** icon next to **Model** in the top left. |
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6. In the **Model drop-down**: choose the model you just downloaded, `OpenAssistant-SFT-7-Llama-30B-GPTQ`. |
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7. If you see an error in the bottom right, ignore it - it's temporary. |
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8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 1024`, `model_type = Llama` |
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9. Click **Save settings for this model** in the top right. |
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10. Click **Reload the Model** in the top right. |
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11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! |
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## Manual instructions for `text-generation-webui` |
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The `compat.no-act-order.safetensors` files 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). |
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[Instructions on using GPTQ 4bit files in text-generation-webui are here](https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-\(4-bit-mode\)). |
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The `latest.act-order.safetensors` files were created using `--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. |
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If you want to use the act-order `safetensors` files and need to update the Triton branch of 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: |
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``` |
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# Clone text-generation-webui, if you don't already have it |
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git clone https://github.com/oobabooga/text-generation-webui |
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# Make a repositories directory |
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mkdir text-generation-webui/repositories |
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cd text-generation-webui/repositories |
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# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui |
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git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa |
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``` |
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Then install this model into `text-generation-webui/models` and launch the UI as follows: |
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``` |
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cd text-generation-webui |
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python server.py --model OpenAssistant-SFT-7-Llama-30B-GPTQ --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want |
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``` |
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To update the CUDA branch of GPTQ-for-LLaMa, you can do the following. **This requires a C/C++ compiler and the CUDA toolkit installed!** |
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``` |
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# Clone text-generation-webui, if you don't already have it |
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git clone https://github.com/oobabooga/text-generation-webui |
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# Make a repositories directory |
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mkdir text-generation-webui/repositories |
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cd text-generation-webui/repositories |
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# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui |
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git clone -b cuda https://github.com/qwopqwop200/GPTQ-for-LLaMa |
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cd GPTQ-for-LLaMa |
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pip uninstall quant-cuda # uninstall existing CUDA version |
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python setup_cuda.py install # install latest version |
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``` |
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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. |
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If you can't update GPTQ-for-LLaMa or don't want to, please use a `compat.no-act-order.safetensor` file. |
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# Original model card |
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``` |
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llama-30b-sft-7: |
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dtype: fp16 |
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log_dir: "llama_log_30b" |
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learning_rate: 1e-5 |
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model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500 |
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#model_name: OpenAssistant/llama-30b-super-pretrain |
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output_dir: llama_model_30b |
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deepspeed_config: configs/zero3_config_sft.json |
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weight_decay: 0.0 |
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residual_dropout: 0.0 |
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max_length: 2048 |
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use_flash_attention: true |
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warmup_steps: 20 |
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gradient_checkpointing: true |
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gradient_accumulation_steps: 12 |
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per_device_train_batch_size: 2 |
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per_device_eval_batch_size: 3 |
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eval_steps: 101 |
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save_steps: 485 |
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num_train_epochs: 4 |
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save_total_limit: 3 |
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use_custom_sampler: true |
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sort_by_length: false |
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#save_strategy: steps |
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save_strategy: epoch |
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datasets: |
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- oasst_export: |
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lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" |
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input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz |
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val_split: 0.05 |
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- vicuna: |
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val_split: 0.05 |
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max_val_set: 800 |
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fraction: 1.0 |
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- dolly15k: |
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val_split: 0.05 |
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max_val_set: 300 |
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- grade_school_math_instructions: |
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val_split: 0.05 |
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- code_alpaca: |
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val_split: 0.05 |
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max_val_set: 250 |
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``` |
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- **OASST dataset paper:** https://arxiv.org/abs/2304.07327 |
