bagel-8x7b-v0.2-AWQ

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Bagel 8X7B v0.2 - AWQ

• Model creator: Jon Durbin
• Original model: Bagel 8X7B v0.2

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Description

This repo contains AWQ model files for Jon Durbin's Bagel 8X7B v0.2.

MIXTRAL AWQ

This is a Mixtral AWQ model.

For AutoAWQ inference, please install AutoAWQ 0.1.8 or later.

Support via Transformers is also available, but currently requires installing Transformers from Github: pip3 install git+https://github.com/huggingface/transformers.git

vLLM: version 0.2.6 is confirmed to support Mixtral AWQs.

TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!)

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 with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

AWQ models are supported by (note that not all of these may support Mixtral models yet - see above):

• Text Generation Webui - using Loader: AutoAWQ
• vLLM - version 0.2.2 or later for support for all model types.
• Hugging Face Text Generation Inference (TGI)
• Transformers version 4.35.0 and later, from any code or client that supports Transformers
• AutoAWQ - for use from Python code

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Repositories available

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

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Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

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Provided files, and AWQ parameters

I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.

Models are released as sharded safetensors files.

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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.

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/bagel-8x7b-v0.2-AWQ.
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: bagel-8x7b-v0.2-AWQ
7. Select Loader: AutoAWQ.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the Text Generation tab and enter a prompt to get started!

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Multi-user inference server: vLLM

Documentation on installing and using vLLM can be found here.

• Please ensure you are using vLLM version 0.2 or later.
• When using vLLM as a server, pass the --quantization awq parameter.

For example:

python3 -m vllm.entrypoints.api_server --model TheBloke/bagel-8x7b-v0.2-AWQ --quantization awq --dtype auto

• When using vLLM from Python code, again set quantization=awq.

For example:

from vllm import LLM, SamplingParams

prompts = [
    "Tell me about AI",
    "Write a story about llamas",
    "What is 291 - 150?",
    "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/bagel-8x7b-v0.2-AWQ", quantization="awq", dtype="auto")

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}")

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Multi-user inference server: Hugging Face Text Generation Inference (TGI)

Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0

Example Docker parameters:

--model-id TheBloke/bagel-8x7b-v0.2-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096

Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):

pip3 install huggingface-hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

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:
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens=128,
                                  do_sample=True,
                                  temperature=0.7,
                                  top_p=0.95,
                                  top_k=40,
                                  repetition_penalty=1.1)

print(f"Model output: ", response)

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Inference from Python code using Transformers

Install the necessary packages

• Requires: Transformers 4.35.0 or later.
• Requires: AutoAWQ 0.1.6 or later.

pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"

Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.

If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:

pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl

If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .

Transformers example code (requires Transformers 4.35.0 and later)

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_name_or_path = "TheBloke/bagel-8x7b-v0.2-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)

# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

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:
'''

# Convert prompt to tokens
tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

generation_params = {
    "do_sample": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "max_new_tokens": 512,
    "repetition_penalty": 1.1
}

# Generate streamed output, visible one token at a time
generation_output = model.generate(
    tokens,
    streamer=streamer,
    **generation_params
)

# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
    tokens,
    **generation_params
)

# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)

# Inference is also possible via Transformers' pipeline
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    **generation_params
)

pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)

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Compatibility

The files provided are tested to work with:

• text-generation-webui using Loader: AutoAWQ.
• vLLM version 0.2.0 and later.
• Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.
• Transformers version 4.35.0 and later.
• AutoAWQ version 0.1.1 and later.

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Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

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: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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Original model card: Jon Durbin's Bagel 8X7B v0.2

A bagel, with everything (except DPO)

Overview

An experimental fine-tune of mixtral-8x7b-v0.1 using bagel

This is the model after the SFT phase, before DPO has been applied.

Hardware kindly provided by Massed Compute

Data sources

Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check

• ai2_arc
  • Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
• airoboros
  • Variety of categories of synthetic instructions generated by gpt-4.
• apps
  • Python coding dataset with 10k problems.
• belebele
  • Multi-lingual reading comprehension dataset.
• bluemoon
  • Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.
• boolq
  • Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
• capybara
  • Multi-turn dataset used to create the capybara models.
• cinematika (instruction and plain text)
  • RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
• drop
  • More reading comprehension.
• emobank
  • Emotion annotations using the Valence-Arousal-Domninance scheme.
• gutenberg (plain text)
  • Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize
• lmsys_chat_1m (only gpt-4 items, also used for DPO)
  • Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
• mathinstruct
  • Composite dataset with a variety of math-related tasks and problem/question formats.
• mmlu
  • Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
• natural_instructions
  • Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
• openbookqa
  • Question answering dataset.
• pippa
  • Deduped version of PIPPA in ShareGPT format.
• piqa
  • Phyiscal interaction question answering.
• python_alpaca
  • Python instruction response pairs, validated as functional.
• rosetta_code
  • Code problems and solutions in a variety of programming languages taken from rosettacode.org.
• slimorca
  • Collection of ~500k gpt-4 verified chats from OpenOrca.
• spider
  • SQL-targeted dataset.
• squad_v2
  • Contextual question answering (RAG).
• synthia
  • GPT-4 generated data using advanced prompting from Migel Tissera.
• winogrande
  • Fill in the blank style prompts.

Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).

How to easily download and use this model

Massed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.

1. For this model rent the Jon Durbin 4xA6000 Virtual Machine
2. After you start your rental you will receive an email with instructions on how to Login to the VM
3. Once inside the VM, open the terminal and run conda activate text-generation-inference
4. Then cd Desktop/text-generation-inference/
5. Run volume=$PWD/data
6. Runmodel=jondurbin/bagel-8x7b-v0.2
7. sudo docker run --gpus '"device=0,1,2,3"' --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model
8. The model will take some time to load...
9. Once loaded the model will be available on port 8080

Sample command within the VM

curl 0.0.0.0:8080/generate \
    -X POST \
    -d '{"inputs":"<|system|>You are a friendly chatbot.\n<|user|>What type of model are you?\n<|assistant|>","parameters":{"do_sample": true, "max_new_tokens": 100, "repetition_penalty": 1.15, "temperature": 0.7, "top_k": 20, "top_p": 0.9, "best_of": 1}}'\
    -H 'Content-Type: application/json'

You can also access the model from outside the VM

curl IP_ADDRESS_PROVIDED_BY_MASSED_COMPUTE_VM:8080/generate \
    -X POST \
    -d '{"inputs":"<|system|>You are a friendly chatbot.\n<|user|>What type of model are you?\n<|assistant|>","parameters":{"do_sample": true, "max_new_tokens": 100, "repetition_penalty": 1.15, "temperature": 0.7, "top_k": 20, "top_p": 0.9, "best_of": 1}}'\
    -H 'Content-Type: application/json

For assistance with the VM join the Massed Compute Discord Server

Prompt formatting

In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta). I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.

This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.

Alpaca (sort of)

Below is an instruction that describes a task.  Write a response that appropriately completes the request.

### Instruction:
{system prompt, if provided}
{instruction}

### Response:

The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an ### Input: block, so the inputs are just in the instruction section.

Vicuna

{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT: 

ChatML (sort of)

I don't really understand the point of having special tokens for <|im_start|> and <|im_end|>, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).

So, instead of:

{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}

I just changed it to:

{bos}{role}
{text}
{eos}

If you really want to use <|im_start|> and <|im_end|>, just update your tokenizer_config.json to use <|im_start|> instead of <s> and <|im_end|> instead of </s> and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.

Llama-2 chat

[INST] <<SYS>>
{system}
<</SYS>>

{instruction} [/INST]

Default via chat template

The model's tokenizer_config.json includes the default chat template (llama-2), so you can simply use the apply_chat_template method to build the full prompt.

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/bagel-8x7b-v0.2')
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

Contribute

If you're interested in new functionality/datasets, take a look at bagel repo and either make a PR or open an issue with details.

To help me with the fine-tuning costs (which are extremely expensive for these large combined datasets):

• https://bmc.link/jondurbin
• ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
• BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf

Guide for certain tasks

RA(G)/contextual question answering

The model was trained to ignore what it thinks it knows, and uses the context to answer the questions, when using the format below. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.

The format for a contextual prompt is as follows:

BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s).  The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION

I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.

• BEGININPUT - denotes a new input block
• BEGINCONTEXT - denotes the block of context (metadata key/value pairs) to associate with the current input block
• ENDCONTEXT - denotes the end of the metadata block for the current input
• [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
• ENDINPUT - denotes the end of the current input block
• [repeat as many input blocks in this format as you want]
• BEGININSTRUCTION - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
• [instruction(s)]
• ENDINSTRUCTION - denotes the end of instruction set

Use a very low temperature!

Here's a trivial, but important example to prove the point:

BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries?  Source?
ENDINSTRUCTION

And the response:

Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123

Summarization

500 samples have been included from this dataset, using the same format as contextual question answering, for example:

BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION

Agent/function calling

The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.

Example prompt:

As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.

Input: I want to know how many times 'Python' is mentioned in my text file.

Available functions:
file_analytics:
  description: This tool performs various operations on a text file.
  params:
    action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
    filters:
      keyword: The word or phrase we want to search for.

Response:

{
  "function": "file_analytics",
  "params": {
    "action": "count_occurrences",
    "filters": {
      "keyword": "Python"
    }
  }
}

reWOO style execution planning

The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!

Example prompt:

Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.

Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?

The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?

Response:

Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:

For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:

import re
import requests

def inject_context(input_text, **context):
    for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
        input_text = input_text.replace(ref, context.get(ref, ""))
    return input_text

def duckduckgo(input_text, **context):
    search_string = inject_context(input_text, **context)
    ... search via duck duck go using search_string
    ... return text content

def link_extractor(input_text, **context):
    input_text = inject_context(input_text, **context)
    return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))

def scrape(input_text, **context):
  input_text = inject_context(input_text, **context)
  text = []
  for link in input_text.splitlines():
    text.append(requests.get(link).text)
  return "\n".join(text)

def infer(input_text, **context)
  prompt = inject_context(input_text, **context)
  ... call model with prompt, return output

def parse_plan(plan):
    method_map = {
      "DuckDuckGo": duckduckgo,
      "HyperlinkExtractor": link_extractor,
      "KnowledgeModel": infer,
      "TextScraper": scrape,
    }
    context = {}
    for line in plan.strip().splitlines():
        if line.startswith("Plan:"):
            print(line)
            continue
        parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
        if not parts:
          if line.startswith("Answer: "):
            return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
          raise RuntimeError("bad format: " + line)
        context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)

Fine-tuning information

You can find charts, and the full configuration used to fine-tune this model on weights and biases

The model was fine-tuned on an 8x a6000 instance, for 4 days, 15 hours, 6 minutes and 42 seconds.

Licence and usage restrictions

The base model is mixtral-8x7b-v0.1, which is licensed as apache-2.0 - no issues there.

The fine-tuning data, however, includes several datasets that have data generated at least in part by OpenAI's gpt-4.

I am not a lawyer, so I can't help determine if this is actually commercially viable, but some questions that often come up are:

• Does the OpenAI ToS apply only to the user who created the dataset initially, and not subsequent models?
• If the dataset was released under a permissive license, but actually includes OpenAI generated data, does that ToS supersede the license?
• Does the dataset fall completely under fair use anyways, since the model isn't really capable of reproducing the entire training set verbatim?

Use your best judgement and seek legal advice if you are concerned about the terms. In any case, by using this model, you agree to completely indemnify me.