Upload new GPTQs with varied parameters

README.md

@@ -1,24 +1,6 @@
 ---
 inference: false
 license: other
-tags:
-- OpenAccess AI Collective
-- MPT
-- axolotl
-datasets:
-- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
-- QingyiSi/Alpaca-CoT
-- teknium/GPTeacher-General-Instruct
-- metaeval/ScienceQA_text_only
-- hellaswag
-- openai/summarize_from_feedback
-- riddle_sense
-- gsm8k
-- camel-ai/math
-- camel-ai/biology
-- camel-ai/physics
-- camel-ai/chemistry
-- winglian/evals
 ---
 
 <!-- header start -->
@@ -27,7 +9,7 @@ datasets:
 </div>
 <div style="display: flex; justify-content: space-between; width: 100%;">
     <div style="display: flex; flex-direction: column; align-items: flex-start;">
-        <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>
     </div>
     <div style="display: flex; flex-direction: column; align-items: flex-end;">
         <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
@@ -37,22 +19,62 @@ datasets:
 
 # OpenAccess AI Collective's Minotaur 13B Fixed GPTQ
 
-These files are GPTQ 4bit model files for [OpenAccess AI Collective's Minotaur 13B Fixed](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed).
+These files are GPTQ model files for [OpenAccess AI Collective's Minotaur 13B Fixed](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed).
 
-It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+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.
+
+These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
 
 ## Repositories available
 
-* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/minotaur-13B-fixed-GPTQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/minotaur-13B-fixed-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/minotaur-13B-fixed-GGML)
-* [OpenAccess AI Collective's unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed)
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed)
+
+## Prompt template: Vicuna
+
+```
+A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
+
+USER: {prompt}
+ASSISTANT:
+
+```
+
+## Provided files
 
-## How to easily download and use this model in text-generation-webui
+Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
 
-Please make sure you're using the latest version of text-generation-webui
+Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
+
+| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
+| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
+| main | 4 | 128 | False | 7.45 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 | 1 | 8.00 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 | 1 | 7.51 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-4bit-128g-actorder_True | 4 | 128 | 1 | 7.26 GB | True | AutoGPTQ | 4-bit, with Act Order androup size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-8bit--1g-actorder_True | 8 | None | 1 | 13.36 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 | 0 | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
+
+## How to download from branches
+
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/minotaur-13B-fixed-GPTQ:gptq-4bit-32g-actorder_True`
+- With Git, you can clone a branch with:
+```
+git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/minotaur-13B-fixed-GPTQ`
+```
+- In Python Transformers code, the branch is the `revision` parameter; see below.
+
+## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
 
 1. Click the **Model tab**.
 2. Under **Download custom model or LoRA**, enter `TheBloke/minotaur-13B-fixed-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/minotaur-13B-fixed-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**.
@@ -62,26 +84,17 @@ Please make sure you're using the latest version of text-generation-webui
   * 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!
 
-## Prompt template
-
-```
-The following is a chat between a USER and a friendly and helpful ASSISTANT.
-USER: prompt
-ASSISTANT:
-```
-
 ## How to use this GPTQ model from Python code
 
 First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
 
-`pip install auto-gptq`
+`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
-import argparse
 
 model_name_or_path = "TheBloke/minotaur-13B-fixed-GPTQ"
 model_basename = "minotaur-13b-GPTQ-4bit-128g.no-act.order"
@@ -91,16 +104,32 @@ use_triton = False
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
 model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
-        model_basename=model_basename,
+        model_basename=model_basename
         use_safetensors=True,
-        trust_remote_code=False,
+        trust_remote_code=True,
         device="cuda:0",
         use_triton=use_triton,
         quantize_config=None)
 
+"""
+To download from a specific branch, use the revision parameter, as in this example:
+
+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'''### Human: {prompt}
-### Assistant:'''
+prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
+
+USER: {prompt}
+ASSISTANT:
+
+'''
 
 print("\n\n*** Generate:")
 
@@ -127,26 +156,18 @@ pipe = pipeline(
 print(pipe(prompt_template)[0]['generated_text'])
 ```
 
-## Provided files
-
-**minotaur-13b-GPTQ-4bit-128g.no-act.order.safetensors**
-
-This will work with AutoGPTQ 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.
+## Compatibility
 
-It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.
+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.
 
-* `minotaur-13b-GPTQ-4bit-128g.no-act.order.safetensors`
-  * Works with AutoGPTQ in CUDA or Triton modes.
-  * Works with GPTQ-for-LLaMa in CUDA mode.  May have issues with GPTQ-for-LLaMa Triton mode.
-  * Works with text-generation-webui, including one-click-installers.
-  * Parameters: Groupsize = 128. Act Order / desc_act = False.
+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.
 
@@ -161,9 +182,9 @@ 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
 
-**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
+**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
 
-**Patreon special mentions**: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
+**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!
 
@@ -175,7 +196,7 @@ Thank you to all my generous patrons and donaters!
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 **[💵 Donate to OpenAccess AI Collective](https://github.com/sponsors/OpenAccess-AI-Collective) to help us keep building great tools and models!**
 
-# Due to a bug, the initial release dropped a few datasets during training. We've corrected the issue and retrained the model
+# Due to a bug, the initial release of Minotaur 13B dropped a few datasets during training. We have corrected the issue and this is the retrained model
 
 The affected datasets include:
 - prose generation