Upload new GPTQs with varied parameters

README.md

@@ -1,146 +1,172 @@
 ---
-license: other
-library_name: transformers
-pipeline_tag: text-generation
-datasets:
-- RyokoAI/ShareGPT52K
-- Hello-SimpleAI/HC3
-tags:
-- koala
-- ShareGPT
-- llama
-- gptq
 inference: false
+license: other
+model_type: llama
 ---
+
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-        <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
+        <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
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-# Koala: A Dialogue Model for Academic Research
-This repo contains the weights of the Koala 7B model produced at Berkeley. It is the result of combining the diffs from https://huggingface.co/young-geng/koala with the original Llama 7B model.
 
-This version has then been quantized to 4-bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+# Young Geng's Koala 7B GPTQ
 
-## My Koala repos
-I have the following Koala model repositories available:
+These files are GPTQ model files for [Young Geng's Koala 7B](https://huggingface.co/young-geng/koala).
 
-**13B models:**
-* [Unquantized 13B model in HF format](https://huggingface.co/TheBloke/koala-13B-HF)
-* [GPTQ quantized 4bit 13B model in `pt` and `safetensors` formats](https://huggingface.co/TheBloke/koala-13B-GPTQ-4bit-128g)
-* [4-bit, 5-bit and 8-bit GGML models for `llama.cpp`](https://huggingface.co/TheBloke/koala-13B-GGML)
+Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
 
-**7B models:**
-* [Unquantized 7B model in HF format](https://huggingface.co/TheBloke/koala-7B-HF)
-* [Unquantized 7B model in GGML format for llama.cpp](https://huggingface.co/TheBloke/koala-7b-ggml-unquantized)
-* [GPTQ quantized 4bit 7B model in `pt` and `safetensors` formats](https://huggingface.co/TheBloke/koala-7B-GPTQ-4bit-128g)
-* [4-bit, 5-bit and 8-bit GGML models for `llama.cpp`](https://huggingface.co/TheBloke/koala-7B-GGML)
+These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
 
-## GETTING GIBBERISH OUTPUT?
+## Repositories available
 
-Please read the sections below carefully.  Gibberish output is expected if you are using the `safetensors` file without the latest GPTQ-for-LLaMa code.
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/koala-7B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/koala-7B-GGML)
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/koala-7B-HF)
 
-Your options are either to update GPTQ-for-LLaMa under `text-generation-webui/repositories` to a more recent version, or use the other file provided, `koala-7B-4bit-128g.no-act-order.ooba.pt` which will work immediately.
+## Prompt template: Koala
 
-Unfortunately right now it is a bit more complext to update GPTQ-for-LLaMa because the most recent code has breaking changes which are not supported by `text-generation-webui`.
-
-Therefore it's currently recommended to use `koala-7B-4bit-128g.no-act-order.ooba.pt`.
+```
+BEGINNING OF CONVERSATION:
+USER: {prompt}
+GPT:
+```
 
 ## Provided files
 
-Three model files are provided. You don't need all three - choose the one that suits your needs best!
-
-Details of the files provided:
-* `koala-7B-4bit-128g.safetensors`
-  * newer `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 koala-7B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors koala-7B-4bit-128g.safetensors`
-* `koala-7B-4bit-128g.no-act-order.ooba.pt`
-  * `pt` format file, created with [oobabooga's older CUDA fork of GPTQ-for-LLaMa](https://github.com/oobabooga/GPTQ-for-LLaMa).
-  * This file is included primarily for Windows users, 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.
-  * The older GPTQ code does not support all the latest features, so the quality may be fractionally lower.
-  * Command to create:
-    * `python3 llama.py koala-7B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save koala-7B-4bit-128g.no-act-order.ooba.pt`
+Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
 
-## How to run in `text-generation-webui`
+Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
 
-File `koala-7B-4bit-128g.no-act-order.ooba.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).
+| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
+| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
+| main | 4 | 128 | False | 4.00 GB | True | GPTQ-for-LLaMa | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
+| gptq-4bit-32g-actorder_True | 4 | 32 | True | 4.28 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
+| gptq-4bit-64g-actorder_True | 4 | 64 | True | 4.02 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-4bit-128g-actorder_True | 4 | 128 | True | 3.90 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-8bit--1g-actorder_True | 8 | None | True | 7.01 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
+| gptq-8bit-128g-actorder_False | 8 | 128 | False | 7.16 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
+| gptq-8bit-128g-actorder_True | 8 | 128 | True | 7.16 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-8bit-64g-actorder_True | 8 | 64 | True | 7.31 GB | False | AutoGPTQ | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. |
 
-The other two model files were created with the latest GPTQ code, and require that the latest GPTQ-for-LLaMa is used inside the UI.
+## How to download from branches
 
-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:
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/koala-7B-GPTQ:gptq-4bit-32g-actorder_True`
+- With Git, you can clone a branch with:
 ```
-git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa
-git clone https://github.com/oobabooga/text-generation-webui
-mkdir -p text-generation-webui/repositories
-ln -s GPTQ-for-LLaMa text-generation-webui/repositories/GPTQ-for-LLaMa
+git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/koala-7B-GPTQ`
 ```
+- In Python Transformers code, the branch is the `revision` parameter; see below.
 
-Then install this model into `text-generation-webui/models` and launch the UI as follows:
-```
-cd text-generation-webui
-python server.py --model koala-7B-GPTQ-4bit-128g --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
-```
+## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 
-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.
+Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 
-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.
+It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
 
-Or just use `koala-7B-4bit-128g.no-act-order.ooba.pt` as mentioned above.
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `TheBloke/koala-7B-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/koala-7B-GPTQ:gptq-4bit-32g-actorder_True`
+  - see Provided Files above for the list of branches for each option.
+3. Click **Download**.
+4. The model will start downloading. Once it's finished it will say "Done"
+5. In the top left, click the refresh icon next to **Model**.
+6. In the **Model** dropdown, choose the model you just downloaded: `koala-7B-GPTQ`
+7. The model will automatically load, and is now ready for use!
+8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
+  * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
+9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
 
-## How the Koala delta weights were merged
+## How to use this GPTQ model from Python code
 
-The Koala delta weights were originally merged using the following commands, producing [koala-7B-HF](https://huggingface.co/TheBloke/koala-7B-HF):
-```
-git clone https://github.com/young-geng/EasyLM
+First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
+
+`GITHUB_ACTIONS=true pip install auto-gptq`
+
+Then try the following example code:
+
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
 
-git clone https://huggingface.co/nyanko7/LLaMA-7B
+model_name_or_path = "TheBloke/koala-7B-GPTQ"
+model_basename = "koala-7b-GPTQ-4bit-128g.no-act.order"
 
-mkdir koala_diffs && cd koala_diffs && wget https://huggingface.co/young-geng/koala/resolve/main/koala_7b_diff_v2
+use_triton = False
 
-cd EasyLM
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
-PYTHON_PATH="${PWD}:$PYTHONPATH" python \
--m EasyLM.models.llama.convert_torch_to_easylm \
---checkpoint_dir=/content/LLaMA-7B \
---output_file=/content/llama-7B-LM \
---streaming=True
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        model_basename=model_basename
+        use_safetensors=True,
+        trust_remote_code=True,
+        device="cuda:0",
+        use_triton=use_triton,
+        quantize_config=None)
 
-PYTHON_PATH="${PWD}:$PYTHONPATH" python \
--m EasyLM.scripts.diff_checkpoint --recover_diff=True \
---load_base_checkpoint='params::/content/llama-7B-LM' \
---load_target_checkpoint='params::/content/koala_diffs/koala_7b_diff_v2' \
---output_file=/content/koala_7b.diff.weights \
---streaming=True
+"""
+To download from a specific branch, use the revision parameter, as in this example:
 
-PYTHON_PATH="${PWD}:$PYTHONPATH" python \
--m EasyLM.models.llama.convert_easylm_to_hf --model_size=7b \
---output_dir=/content/koala-7B-HF \
---load_checkpoint='params::/content/koala_7b.diff.weights' \
---tokenizer_path=/content/LLaMA-7B/tokenizer.model
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        revision="gptq-4bit-32g-actorder_True",
+        model_basename=model_basename,
+        use_safetensors=True,
+        trust_remote_code=True,
+        device="cuda:0",
+        quantize_config=None)
+"""
+
+prompt = "Tell me about AI"
+prompt_template=f'''BEGINNING OF CONVERSATION:
+USER: {prompt}
+GPT:
+'''
+
+print("\n\n*** Generate:")
+
+input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
+print(tokenizer.decode(output[0]))
+
+# Inference can also be done using transformers' pipeline
+
+# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
+logging.set_verbosity(logging.CRITICAL)
+
+print("*** Pipeline:")
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.95,
+    repetition_penalty=1.15
+)
+
+print(pipe(prompt_template)[0]['generated_text'])
 ```
 
+## Compatibility
+
+The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
+
+ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
+
 <!-- footer start -->
 ## Discord
 
 For further support, and discussions on these models and AI in general, join us at:
 
-[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
 
 ## Thanks, and how to contribute.
 
@@ -155,13 +181,19 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 * Patreon: https://patreon.com/TheBlokeAI
 * Ko-Fi: https://ko-fi.com/TheBlokeAI
 
-**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
+**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
+
+**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
 
 Thank you to all my generous patrons and donaters!
+
 <!-- footer end -->
-## Further info
 
-Check out the following links to learn more about the Berkeley Koala model.
+# Original model card: Young Geng's Koala 7B
+
+
+# Koala: A Dialogue Model for Academic Research
+This repo contains the weights diff against the base LLaMA for the Koala model. Check out the following links to get started:
 * [Blog post](https://bair.berkeley.edu/blog/2023/04/03/koala/)
 * [Online demo](https://koala.lmsys.org/)
 * [EasyLM: training and serving framework on GitHub](https://github.com/young-geng/EasyLM)
@@ -173,3 +205,4 @@ The model weights are intended for academic research only, subject to the
 [Terms of Use of the data generated by OpenAI](https://openai.com/policies/terms-of-use),
 and [Privacy Practices of ShareGPT](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb).
 Any other usage of the model weights, including but not limited to commercial usage, is strictly prohibited.
+Please contact us If you find any potential violations. Our training and inference code is released under the Apache License 2.0.