Upload README.md

README.md

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+---
+base_model: https://huggingface.co/openaccess-ai-collective/minotaur-13b
+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
+license: apache-2.0
+model_creator: Open Access AI Collective
+model_name: Minotaur 13B
+model_type: llama
+prompt_template: 'You are a helpful AI assistant.
+
+
+  USER: {prompt}
+
+  ASSISTANT:
+
+  '
+quantized_by: TheBloke
+tags:
+- OpenAccess AI Collective
+- MPT
+- axolotl
+---
+
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+
+# Minotaur 13B - AWQ
+- Model creator: [Open Access AI Collective](https://huggingface.co/openaccess-ai-collective)
+- Original model: [Minotaur 13B](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
+
+<!-- description start -->
+## Description
+
+This repo contains AWQ model files for [Open Access AI Collective's Minotaur 13B](https://huggingface.co/openaccess-ai-collective/minotaur-13b).
+
+
+### About AWQ
+
+AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
+
+It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
+<!-- description end -->
+<!-- repositories-available start -->
+## Repositories available
+
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/minotaur-13B-AWQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/minotaur-13B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/minotaur-13B-GGUF)
+* [Open Access AI Collective's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
+<!-- repositories-available end -->
+
+<!-- prompt-template start -->
+## Prompt template: Vicuna-Short
+
+```
+You are a helpful AI assistant.
+
+USER: {prompt}
+ASSISTANT:
+
+```
+
+<!-- prompt-template end -->
+<!-- licensing start -->
+## Licensing
+
+The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.
+
+As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
+
+In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Open Access AI Collective's Minotaur 13B](https://huggingface.co/openaccess-ai-collective/minotaur-13b).
+<!-- licensing end -->
+<!-- README_AWQ.md-provided-files start -->
+## Provided files and AWQ parameters
+
+For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
+
+Models are released as sharded safetensors files.
+
+| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
+| ------ | ---- | -- | ----------- | ------- | ---- |
+| [main](https://huggingface.co/TheBloke/minotaur-13B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
+
+<!-- README_AWQ.md-provided-files end -->
+
+<!-- README_AWQ.md-use-from-vllm start -->
+## Serving this model from vLLM
+
+Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
+
+- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
+
+```shell
+python3 python -m vllm.entrypoints.api_server --model TheBloke/minotaur-13B-AWQ --quantization awq
+```
+
+When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+llm = LLM(model="TheBloke/minotaur-13B-AWQ", quantization="awq")
+
+outputs = llm.generate(prompts, sampling_params)
+
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+<!-- README_AWQ.md-use-from-vllm start -->
+
+<!-- README_AWQ.md-use-from-python start -->
+## How to use this AWQ model from Python code
+
+### Install the necessary packages
+
+Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
+
+```shell
+pip3 install autoawq
+```
+
+If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
+
+```shell
+pip3 uninstall -y autoawq
+git clone https://github.com/casper-hansen/AutoAWQ
+cd AutoAWQ
+pip3 install .
+```
+
+### You can then try the following example code
+
+```python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer
+
+model_name_or_path = "TheBloke/minotaur-13B-AWQ"
+
+# Load model
+model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
+                                          trust_remote_code=False, safetensors=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
+
+prompt = "Tell me about AI"
+prompt_template=f'''You are a helpful AI assistant.
+
+USER: {prompt}
+ASSISTANT:
+
+'''
+
+print("\n\n*** Generate:")
+
+tokens = tokenizer(
+    prompt_template,
+    return_tensors='pt'
+).input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(
+    tokens,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    top_k=40,
+    max_new_tokens=512
+)
+
+print("Output: ", tokenizer.decode(generation_output[0]))
+
+# Inference can also be done using transformers' pipeline
+from transformers import pipeline
+
+print("*** Pipeline:")
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.1
+)
+
+print(pipe(prompt_template)[0]['generated_text'])
+```
+<!-- README_AWQ.md-use-from-python end -->
+
+<!-- README_AWQ.md-compatibility start -->
+## Compatibility
+
+The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
+
+[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
+<!-- README_AWQ.md-compatibility end -->
+
+<!-- footer start -->
+<!-- 200823 -->
+## 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!
+
+Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
+
+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**: Aemon Algiz.
+
+**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
+
+
+Thank you to all my generous patrons and donaters!
+
+And thank you again to a16z for their generous grant.
+
+<!-- footer end -->
+
+# Original model card: Open Access AI Collective's Minotaur 13B
+
+
+[<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!**
+
+# DEPRECATION! This model has been superseded by Minotaur 13B Fixed
+
+https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed
+
+Due to a bug, the initial release dropped a few datasets during training. We've corrected the issue and retrained the model
+
+
+# =============================================================================================================================
+
+# =============================================================================================================================
+
+# =============================================================================================================================
+
+# 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 [wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org)
+
+# Prompts
+Chat only style prompts using `USER:`,`ASSISTANT:`.
+
+<img src="https://huggingface.co/openaccess-ai-collective/minotaur-13b/resolve/main/minotaur.png" alt="minotaur" width="600" height="500"/>
+
+# 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, derived from the `train` split
+  - [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented, derived from the `train` split
+  - prose generation
+
+# 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 LLaMA-13B, 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.
+```