Initial GPTQ model commit

README.md

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+---
+inference: false
+license: other
+model_type: llama
+tags:
+- llama
+- alpaca
+- vicuna
+- uncensored
+- cot
+- chain of thought
+- story
+- adventure
+- roleplay
+- rp
+- merge
+- mix
+- instruct
+- wizardlm
+- superhot
+- supercot
+- manticore
+- hippogriff
+---
+
+<!-- header start -->
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+    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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+    </div>
+</div>
+<!-- header end -->
+
+# CalderaAI's 30B Epsilon GPTQ
+
+These files are GPTQ model files for [CalderaAI's 30B Epsilon](https://huggingface.co/CalderaAI/30B-Epsilon).
+
+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.
+
+
+## Repositories available
+
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/30B-Epsilon-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/30B-Epsilon-GGML)
+* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/CalderaAI/30B-Epsilon)
+
+## Prompt template: Alpaca
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction: {prompt}
+
+### Response:
+```
+
+## Provided files
+
+Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
+
+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 | None | True | 16.94 GB | True | AutoGPTQ | 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 | 16937476440.00 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 | 16937476440.00 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 | 16937476440.00 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 | 16937476440.00 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 | 16937476440.00 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 | 16937476440.00 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/30B-Epsilon-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/30B-Epsilon-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/30B-Epsilon-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/30B-Epsilon-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: `30B-Epsilon-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 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`
+
+Then try the following example code:
+
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+
+model_name_or_path = "TheBloke/30B-Epsilon-GPTQ"
+model_basename = "gptq_model-4bit--1g"
+
+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,
+        use_safetensors=True,
+        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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction: {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/theblokeai)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+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.
+
+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.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
+
+**Patreon special mentions**: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse
+
+
+Thank you to all my generous patrons and donaters!
+
+<!-- footer end -->
+
+# Original model card: CalderaAI's 30B Epsilon
+
+
+## 30B-Epsilon
+
+Epsilon is an instruct based general purpose model assembled from hand picked models and LoRAs.
+There is no censorship and it follows instructions in the Alpaca format. This means you can create
+your own rules in the context memory of your inference system of choice [mainly KoboldAI or Text
+Generation Webui and chat UIs like SillyTavern and so on].
+
+## Composition:
+
+This model is the result of an experimental use of LoRAs on language models and model merges.
+[] = applied as LoRA to a composite model | () = combined as composite models
+30B-Epsilon = [SuperCOT[SuperHOT-prototype13b-8192[(wizardlmuncensored+((hippogriff+manticore)+(StoryV2))]
+
+Alpaca's instruct format can be used to do many things, including control of the terms of behavior
+between a user and a response from an agent in chat. Below is an example of a command injected into
+memory.
+
+```
+### Instruction:
+Make Narrator function as a text based adventure game that responds with verbose, detailed, and creative descriptions of what happens next after Player's response.
+Make Player function as the player input for Narrator's text based adventure game, controlling a character named (insert character name here, their short bio, and
+whatever quest or other information to keep consistent in the interaction).
+
+### Response:
+{an empty new line here}
+```
+
+All datasets from all models and LoRAs used were documented and reviewed as model candidates for merging.
+Model candidates were based on five core principles: creativity, logic, inference, instruction following,
+and longevity of trained responses. SuperHOT-prototype30b-8192 was used in this mix, not the 8K version;
+the prototype LoRA seems to have been removed [from HF] as of this writing. The GPT4Alpaca LoRA from
+Chansung was removed from this amalgam following a thorough review of where censorship and railroading
+the user came from in 33B-Lazarus. This is not a reflection of ChanSung's excellent work - it merely did
+not fit the purpose of this model.
+
+## Language Models and LoRAs Used Credits:
+
+manticore-30b-chat-pyg-alpha [Epoch0.4] by openaccess-ai-collective
+
+https://huggingface.co/openaccess-ai-collective/manticore-30b-chat-pyg-alpha
+
+hippogriff-30b-chat by openaccess-ai-collective
+
+https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat
+
+WizardLM-33B-V1.0-Uncensored by ehartford
+
+https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored
+
+Storytelling-LLaMa-LoRA [30B, Version 2] by GamerUnTouch
+
+https://huggingface.co/GamerUntouch/Storytelling-LLaMa-LoRAs
+
+SuperCOT-LoRA [30B] by kaiokendev
+
+https://huggingface.co/kaiokendev/SuperCOT-LoRA
+
+SuperHOT-LoRA-prototype30b-8192 [30b, not 8K version, but a removed prototype] by kaiokendev
+
+https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test [Similar LoRA to one since removed that was used in making this model.]
+
+Also thanks to Meta for LLaMA and to each and every one of you
+who developed these fine-tunes and LoRAs.