Upload new GPTQs with varied parameters

README.md

@@ -1,16 +1,20 @@
 ---
-license: other
 datasets:
 - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
 inference: false
+license: other
+model_type: llama
+tags:
+- uncensored
 ---
+
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@@ -18,60 +22,153 @@ inference: false
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-# WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct
+# Eric Hartford's WizardLM 30B Uncensored GPTQ
+
+These files are GPTQ model files for [Eric Hartford's WizardLM 30B Uncensored](https://huggingface.co/ehartford/WizardLM-30B-Uncensored).
+
+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 files are GPTQ 4bit model files for [Eric Hartford's 'uncensored' version of WizardLM](https://huggingface.co/ehartford/WizardLM-30B-Uncensored).
+These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
 
-It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+## Repositories available
 
-## Other repositories available
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGML)
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/WizardLM-30B-Uncensored)
+
+## Prompt template: WizardLM
+
+```
+{prompt}
+### Response:
+```
+
+## Provided files
 
-* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GPTQ)
-* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGML)
-* [Eric's unquantised model in HF format](https://huggingface.co/ehartford/WizardLM-30B-Uncensored)
+Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
 
-## How to easily download and use this model in text-generation-webui
+Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
 
-Open the text-generation-webui UI as normal.
+| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
+| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
+| main | 4 | None | True | 16.94 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 | 19.44 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 | 18.18 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 | 17.55 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 | 32.99 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 | 33.73 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
+| gptq-3bit--1g-actorder_True | 3 | None | True | 12.92 GB | False | AutoGPTQ | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
+| gptq-3bit-128g-actorder_False | 3 | 128 | False | 13.51 GB | False | AutoGPTQ | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
+
+## How to download from branches
+
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/WizardLM-30B-uncensored-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/WizardLM-30B-uncensored-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/WizardLM-30B-Uncensored-GPTQ`.
+2. Under **Download custom model or LoRA**, enter `TheBloke/WizardLM-30B-uncensored-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/WizardLM-30B-uncensored-GPTQ:gptq-4bit-32g-actorder_True`
+  - see Provided Files above for the list of branches for each option.
 3. Click **Download**.
-4. Wait until it says it's finished downloading.
-5. Click the **Refresh** icon next to **Model** in the top left.
-6. In the **Model drop-down**: choose the model you just downloaded, `WizardLM-30B-Uncensored-GPTQ`.
-7. If you see an error in the bottom right, ignore it - it's temporary.
-8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = None`, `model_type = Llama`
-9. Click **Save settings for this model** in the top right.
-10. Click **Reload the Model** in the top right.
-11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
+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: `WizardLM-30B-uncensored-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!
 
-## Provided files
+## How to use this GPTQ model from Python code
+
+First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
+
+`GITHUB_ACTIONS=true pip install auto-gptq`
 
-**Compatible file - WizardLM-30B-uncensored-GPTQ-4bit.act-order.safetensors**
+Then try the following example code:
 
-In the `main` branch - the default one - you will find `WizardLM-30B-uncensored-GPTQ-4bit.act-order.safetensors`
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
 
-This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility
+model_name_or_path = "TheBloke/WizardLM-30B-uncensored-GPTQ"
+model_basename = "WizardLM-30B-Uncensored-GPTQ-4bit--1g.act.order"
 
-It was created with the `--act-order` parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui.
+use_triton = False
 
-* `wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors`
-  * Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
-  * Works with AutoGPTQ. Use `strict=False` to load.
-  * Works with text-generation-webui one-click-installers
-  * Parameters: Groupsize = None. act-order.
-  * Command used to create the GPTQ:
-    ```
-    python llama.py /workspace/models/ehartford_WizardLM-30B-Uncensored  wikitext2 --wbits 4 --true-sequential --act-order  --save_safetensors /workspace/eric-30B/gptq/WizardLM-30B-Uncensored-GPTQ-4bit.act-order.safetensors
-    ```
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        model_basename=model_basename
+        use_safetensors=True,
+        trust_remote_code=False,
+        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=False,
+        device="cuda:0",
+        quantize_config=None)
+"""
+
+prompt = "Tell me about AI"
+prompt_template=f'''{prompt}
+### Response:
+'''
+
+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.
 
@@ -86,18 +183,22 @@ 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 -->
-# WizardLM-30B-Uncensored original model card
+
+# Original model card: Eric Hartford's WizardLM 30B Uncensored
 
 This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed.  The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
 
 Shout out to the open source AI/ML community, and everyone who helped me out.
 
-Note:
-An uncensored model has no guardrails.
+Note:  
+An uncensored model has no guardrails.  
 You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.
 Publishing anything this model generates is the same as publishing it yourself.
 You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.