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--- |
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inference: false |
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license: other |
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--- |
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# LmSys' Vicuna 33B 1.3 (final) GPTQ |
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These files are GPTQ 4bit model files for [LmSys' Vicuna 33B 1.3 (final)](https://huggingface.co/lmsys/vicuna-33b-v1.3) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test). |
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It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). |
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**This is an experimental new GPTQ which offers up to 8K context size** |
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The increased context is tested to work with [ExLlama](https://github.com/turboderp/exllama), via the latest release of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). |
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It has also been tested from Python code using AutoGPTQ, and `trust_remote_code=True`. |
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Code credits: |
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- Original concept and code for increasing context length: [kaiokendev](https://huggingface.co/kaiokendev) |
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- Updated Llama modelling code that includes this automatically via trust_remote_code: [emozilla](https://huggingface.co/emozilla). |
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Please read carefully below to see how to use it. |
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**NOTE**: Using the full 8K context on a 30B model will exceed 24GB VRAM. |
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## Repositories available |
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* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GGML) |
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* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Vicuna-33B-1-3-SuperHOT-8K-fp16) |
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* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-33b-v1.3) |
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## How to easily download and use this model in text-generation-webui with ExLlama |
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Please make sure you're using the latest version of text-generation-webui |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GPTQ`. |
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3. Click **Download**. |
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4. The model will start downloading. Once it's finished it will say "Done" |
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5. Untick **Autoload the model** |
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6. In the top left, click the refresh icon next to **Model**. |
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7. In the **Model** dropdown, choose the model you just downloaded: `Vicuna-33B-1-3-SuperHOT-8K-GPTQ` |
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8. To use the increased context, set the **Loader** to **ExLlama**, set **max_seq_len** to 8192 or 4096, and set **compress_pos_emb** to **4** for 8192 context, or to **2** for 4096 context. |
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9. Now click **Save Settings** followed by **Reload** |
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10. The model will automatically load, and is now ready for use! |
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11. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! |
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## How to use this GPTQ model from Python code with AutoGPTQ |
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First make sure you have AutoGPTQ and Einops installed: |
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``` |
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pip3 install einops auto-gptq |
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``` |
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Then run the following code. Note that in order to get this to work, `config.json` has been hardcoded to a sequence length of 8192. |
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If you want to try 4096 instead to reduce VRAM usage, please manually edit `config.json` to set `max_position_embeddings` to the value you want. |
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```python |
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from transformers import AutoTokenizer, pipeline, logging |
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig |
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import argparse |
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model_name_or_path = "TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GPTQ" |
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model_basename = "vicuna-33b-1.3-superhot-8k-GPTQ-4bit--1g.act.order" |
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use_triton = False |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) |
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model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, |
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model_basename=model_basename, |
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use_safetensors=True, |
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trust_remote_code=True, |
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device_map='auto', |
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use_triton=use_triton, |
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quantize_config=None) |
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model.seqlen = 8192 |
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# Note: check the prompt template is correct for this model. |
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prompt = "Tell me about AI" |
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prompt_template=f'''USER: {prompt} |
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ASSISTANT:''' |
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print("\n\n*** Generate:") |
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input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() |
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output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) |
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print(tokenizer.decode(output[0])) |
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# Inference can also be done using transformers' pipeline |
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# Prevent printing spurious transformers error when using pipeline with AutoGPTQ |
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logging.set_verbosity(logging.CRITICAL) |
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print("*** Pipeline:") |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=512, |
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temperature=0.7, |
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top_p=0.95, |
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repetition_penalty=1.15 |
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) |
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print(pipe(prompt_template)[0]['generated_text']) |
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``` |
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## Using other UIs: monkey patch |
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Provided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev. |
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It can be theoretically be added to any Python UI or custom code to enable the same result as `trust_remote_code=True`. I have not tested this, and it should be superseded by using `trust_remote_code=True`, but I include it for completeness and for interest. |
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## Provided files |
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**vicuna-33b-1.3-superhot-8k-GPTQ-4bit--1g.act.order.safetensors** |
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This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead. |
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It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible. |
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* `vicuna-33b-1.3-superhot-8k-GPTQ-4bit--1g.act.order.safetensors` |
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* Works for use with ExLlama with increased context (4096 or 8192) |
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* Works with AutoGPTQ in Python code, including with increased context, if `trust_remote_code=True` is set. |
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* Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. May have issues with GPTQ-for-LLaMa Triton mode. |
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* Works with text-generation-webui, including one-click-installers. |
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* Parameters: Groupsize = -1. Act Order / desc_act = True. |
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<!-- footer start --> |
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## Discord |
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For further support, and discussions on these models and AI in general, join us at: |
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[TheBloke AI's Discord server](https://discord.gg/theblokeai) |
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## Thanks, and how to contribute. |
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Thanks to the [chirper.ai](https://chirper.ai) team! |
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I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. |
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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. |
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
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* Patreon: https://patreon.com/TheBlokeAI |
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* Ko-Fi: https://ko-fi.com/TheBlokeAI |
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**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. |
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**Patreon special mentions**: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski. |
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Thank you to all my generous patrons and donaters! |
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<!-- footer end --> |
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# Original model card: Kaio Ken's SuperHOT 8K |
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### SuperHOT Prototype 2 w/ 8K Context |
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This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k). |
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Tests have shown that the model does indeed leverage the extended context at 8K. |
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You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192** |
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#### Looking for Merged & Quantized Models? |
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- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors) |
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- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors) |
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#### Training Details |
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I trained the LoRA with the following configuration: |
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- 1200 samples (~400 samples over 2048 sequence length) |
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- learning rate of 3e-4 |
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- 3 epochs |
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- The exported modules are: |
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- q_proj |
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- k_proj |
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- v_proj |
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- o_proj |
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- no bias |
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- Rank = 4 |
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- Alpha = 8 |
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- no dropout |
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- weight decay of 0.1 |
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- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5 |
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- Trained on 4-bit base model |
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# Original model card: LmSys' Vicuna 33B 1.3 (final) |
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# Vicuna Model Card |
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## Model Details |
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Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. |
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- **Developed by:** [LMSYS](https://lmsys.org/) |
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- **Model type:** An auto-regressive language model based on the transformer architecture. |
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- **License:** Non-commercial license |
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- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). |
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### Model Sources |
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- **Repository:** https://github.com/lm-sys/FastChat |
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- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ |
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- **Paper:** https://arxiv.org/abs/2306.05685 |
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- **Demo:** https://chat.lmsys.org/ |
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## Uses |
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The primary use of Vicuna is research on large language models and chatbots. |
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The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. |
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## How to Get Started with the Model |
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Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. |
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APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. |
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## Training Details |
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Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. |
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The training data is around 140K conversations collected from ShareGPT.com. |
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See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). |
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## Evaluation |
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Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf). |
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## Difference between different versions of Vicuna |
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See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md) |
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