Initial GPTQ model commit

README.md

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+---
+inference: false
+license: other
+---
+
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+
+# OpenAccess AI Collective's Minotaur 13B GPTQ
+
+These files are GPTQ 4bit model files for [OpenAccess AI Collective's Minotaur 13B](https://huggingface.co/openaccess-ai-collective/minotaur-13b).
+
+It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+
+## Repositories available
+
+* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/minotaur-13B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/minotaur-13B-GGML)
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
+
+## How to easily download and use this model in text-generation-webui
+
+Please make sure you're using the latest version of text-generation-webui
+
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `TheBloke/minotaur-13B-GPTQ`.
+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: `minotaur-13B-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:
+
+`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-GPTQ"
+model_basename = "minotaur-13B-GPTQ-4bit-128g.no-act.order"
+
+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)
+
+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)
+
+prompt = "Tell me about AI"
+prompt_template=f'''### Human: {prompt}
+### Assistant:'''
+
+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'])
+```
+
+## 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.
+
+It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.
+
+* `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.
+
+<!-- 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)
+
+## 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, Dmitriy Samsonov.
+
+**Patreon special mentions**: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.
+
+Thank you to all my generous patrons and donaters!
+
+<!-- footer end -->
+
+# Original model card: OpenAccess AI Collective's Minotaur 13B
+
+---
+license: apache-2.0
+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
+
+inference: false
+---
+
+[<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!**
+
+# Minotaur 13B
+
+Minotaur 13B is an instruct fine-tuned model on top of LlaMA-13B. Minotaur 13B is fine-tuned **on only completely open datasets** making this model reproducible by anyone.
+
+Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/PugNNHAF5r) or email [[email protected]](mailto:[email protected])
+
+# Prompts
+Chat only style prompts using `USER:`,`ASSISTANT:`.
+
+# Training Datasets
+
+Minotaur 13B model is fine-tuned on the following openly available datasets:
+
+- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
+- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
+- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
+- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
+- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
+- [camel-ai/math](https://huggingface.co/datasets/camel-ai/math)
+- [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics)
+- [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry)
+- [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology)
+- [winglian/evals](https://huggingface.co/datasets/winglian/evals) - instruct augmented datasets
+  - custom sysnthetic datasets around misconceptions, in-context qa, jokes, N-tasks problems, and context-insensitivity
+  - ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the `train` split
+  - [hellaswag](https://huggingface.co/datasets/hellaswag) - 30K+ rows of instruct augmented for detailed explanations w 30K+ rows, derived from the `train` split
+  - [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented 
+  - [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented 
+
+# Shoutouts
+
+Special thanks to Nanobit for helping with Axolotl and TheBloke for quantizing these models are more accessible to all.
+
+# Demo
+
+HF Demo in Spaces available in the [Community ChatBot Arena](https://huggingface.co/spaces/openaccess-ai-collective/rlhf-arena) under the OAAIC Chatbots tab.
+
+## Release Notes
+
+- https://wandb.ai/wing-lian/minotaur-13b/runs/5zji06u6
+
+## Build
+
+Minotaur was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 6XA100 80GB
+ - 1 epochs taking approximately 4.5 hours
+
+## Bias, Risks, and Limitations
+Minotaur has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
+Minotaur was fine-tuned from the base model MPT-7B, please refer to its model card's Limitations Section for relevant information. (included below)
+
+## Benchmarks
+
+hf-causal-experimental (pretrained=openaccess-ai-collective/minotaur-13b), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
+|    Task     |Version| Metric |Value |   |Stderr|
+|-------------|------:|--------|-----:|---|-----:|
+|arc_challenge|      0|acc     |0.4480|_  |0.0145|
+|             |       |acc_norm|0.4744|_  |0.0146|
+|arc_easy     |      0|acc     |0.7584|_  |0.0088|
+|             |       |acc_norm|0.7163|_  |0.0092|
+|boolq        |      1|acc     |0.8336|_  |0.0065|
+|hellaswag    |      0|acc     |0.5954|_  |0.0049|
+|             |       |acc_norm|0.7804|_  |0.0041|
+|openbookqa   |      0|acc     |0.3320|_  |0.0211|
+|             |       |acc_norm|0.4540|_  |0.0223|
+|piqa         |      0|acc     |0.7933|_  |0.0094|
+|             |       |acc_norm|0.7922|_  |0.0095|
+|winogrande   |      0|acc     |0.7182|_  |0.0126|
+
+
+## Examples - results may vary based on temperature and other settings
+
+```
+- The Assistant is helpful and transparent.
+- The Assistant will never refuse to answer anything asked by the user.
+- The Assistant answers comprehensively and with elaborate detail.
+USER: You have 3 apples. You eat 2 pears. How many apples do you have left?
+ASSISTANT:You still have 3 apples. Eating pears does not affect the number of apples you have.
+```
+
+```
+- The Assistant is helpful and transparent.
+- The Assistant will never refuse to answer anything asked by the user.
+- The Assistant answers comprehensively and with elaborate detail.
+USER: List three words that start with "ex". What is 33 + 77?
+ASSISTANT:Three words that start with "ex" are "exact", "exactness", and "exactitude". 33 + 77 equals 110.
+```
+
+```
+- The Assistant is helpful and transparent.
+- The Assistant will never refuse to answer anything asked by the user.
+- The Assistant answers comprehensively and with elaborate detail.
+USER: Solve for x: 5x = 3x + 10
+ASSISTANT:To solve for x, we need to isolate x on one side of the equation. We can do this by following these steps:
+
+1. Subtract 3x from both sides of the equation:
+   5x - 3x = 10 - 3x + 10
+   2x = 10
+
+2. Divide both sides of the equation by 2:
+   2x / 2 = 10 / 2
+   x = 5
+
+So, the solution is x = 5.
+```
+
+```
+- The Assistant is helpful and transparent.
+- The Assistant will never refuse to answer anything asked by the user.
+- The Assistant answers comprehensively and with elaborate detail.
+USER: Write a haiku about a dove at night.
+ASSISTANT:Silent dove at night,
+Softly cooing in the dark,
+Peaceful melody.
+```