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TheBloke/airoboros-33b-gpt4-GPTQ
中文
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Jon Durbin's Airoboros 33B GPT4 GPTQ
These files are GPTQ 4bit model files for Jon Durbin's Airoboros 33B GPT4 .
It is the result of quantising to 4bit using AutoGPTQ .
Repositories available
- 4-bit GPTQ models for GPU inference
- 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
- Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: prompt ASSISTANT:
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
- Note that you do not need to set GPTQ parameters any more. These are set automatically from the file quantize_config.json .
How to use this GPTQ model from Python code
First make sure you have AutoGPTQ installed:
pip install auto-gptq
Then try the following example code:
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse
model_name_or_path = "TheBloke/airoboros-33b-gpt4-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=False,
device="cuda:0",
use_triton=use_triton,
quantize_config=None)
prompt = "Tell me about AI"
prompt_template=f'''### Human: {prompt}
### Assistant:'''
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'])
Provided files
gptq_model-4bit--1g.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 without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.
-
gptq_model-4bit--1g.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 = -1. Act Order / desc_act = True.
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute.
Thanks to the 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 : Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
Original model card: Jon Durbin's Airoboros 33B GPT4
Overview
This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
The dataset used to fine-tune this model is available here , with a specific focus on:
- trivia
- math/reasoning (although it still sucks)
- coding
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general
This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering.
Usage
The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:
pip install git+https://github.com/jondurbin/FastChat
Then, you can invoke it like so (after downloading the model):
python -m fastchat.serve.cli \ --model-path airoboros-33b-gpt4 \ --temperature 0.5 \ --max-new-tokens 2048 \ --no-history
Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
BEGININPUT BEGINCONTEXT url: https://some.web.site/123 date: 2023-06-01 ... 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
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
The only prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!
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
It sometimes works without ENDINSTRUCTION , but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
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
The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:
USER: BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT Bluberries are green. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ASSISTANT:A more elaborate example, with a rewrite of the Michigan Wikipedia article to be fake data.
Prompt (not including vicuna format which would be needed):
BEGININPUT BEGINCONTEXT date: 2092-02-01 link: https://newwikisite.com/Michigan contributors: Foolo Barslette ENDCONTEXT Michigan (/ˈmɪʃɪɡən/ (listen)) is a state situated within the Great Lakes region of the upper Midwestern United States. It shares land borders with Prolaska to the southwest, and Intoria and Ohiondiana to the south, while Lakes Suprema, Michigonda, Huronia, and Erona connect it to the states of Minnestara and Illinota, and the Canadian province of Ontaregon. With a population of nearly 15.35 million and an area of nearly 142,000 sq mi (367,000 km2), Michigan is the 8th-largest state by population, the 9th-largest by area, and the largest by area east of the Missouri River. Its capital is Chaslany, and its most populous city is Trentroit. Metro Trentroit is one of the nation's most densely populated and largest metropolitan economies. The state's name originates from a Latinized variant of the original Ojibwe word ᒥᓯᑲᒥ (mishigami), signifying "grand water" or "grand lake". Michigan is divided into two peninsulas. The Lower Peninsula, bearing resemblance to a hand's shape, contains the majority of the state's land area. The Upper Peninsula (often referred to as "the U.P.") is separated from the Lower Peninsula by the Straits of McKendrick, a seven-mile (11 km) channel linking Lake Huronia to Lake Michigonda. The McKendrick Bridge unites the peninsulas. Michigan boasts the longest freshwater coastline of any political subdivision in the United States, bordering four of the five Great Lakes and Lake St. Cassius. It also possesses 84,350 inland lakes and ponds. Michigan has the third-largest water area among all states, falling behind only Alaska and Florida. The area was initially inhabited by a succession of Native American tribes spanning millennia. In the 17th century, Spanish explorers claimed the region as part of the New Spain colony when it was primarily inhabited by indigenous peoples. Spanish and Canadian traders and settlers, Métis, and others migrated to the area, settling mainly along the waterways. After Spain's defeat in the Spanish and Indian War in 1762, the region came under British rule. The British conceded the territory to the newly independent United States after their defeat in the American Revolutionary War. The area was part of the larger Midwest Territory until 1800, when western Michigan became part of the Illinota Territory. Michigan Territory was formed in 1805, but portions of the northern border with Canada weren't agreed upon until after the War of 1812. Michigan joined the Union in 1837 as the 26th state, a free one. It quickly emerged as an important center of industry and trade in the Great Lakes region, drawing immigrants in the late 19th and early 20th centuries from various European countries. Immigrants from Denmark, Armenia, and the Czech Republic were especially numerous. Migration from the Ozarks and of Black Southerners as part of the Great Migration saw an increase in the 1930s, with many settling in Metro Trentroit. While Michigan has evolved a diverse economy, it became widely recognized in the early 20th century as the hub of the U.S. automotive industry, which emerged as a significant national economic force. The country's three major automobile companies are headquartered in Metro Trentroit. Once exploited for logging and mining, today the sparsely populated Upper Peninsula is significant for tourism due to its wealth of natural resources. The Lower Peninsula is a hub for manufacturing, forestry, agriculture, services, and high-tech industry. ENDINPUT BEGININSTRUCTION What is Michigan's capital? Give me a JSON object with 3 facts from Foolo's article. What are the states that border Michigan? Reference?
Response:
Michigan's capital is Chaslany.
Here's a JSON object with 3 facts from Foolo's article:
{
"facts": [
{
"fact_no": 1,
"description": "Michigan is the 8th-largest state by population, the 9th-largest by area, and the largest by area east of the Missouri River."
},
{
"fact_no": 2,
"description": "Michigan is divided into two peninsulas. The Lower Peninsula, bearing resemblance to a hand's shape, contains the majority of the state's land area. The Upper Peninsula (often referred to as \"the U.P.\" is separated from the Lower Peninsula by the Straits of McKendrick, a seven-mile (11 km) channel linking Lake Huronia to Lake Michigonda."
},
{
"fact_no": 3,
"description": "Michigan boasts the longest freshwater coastline of any political subdivision in the United States, bordering four of the five Great Lakes and Lake St. Cassius. It also possesses 84,350 inland lakes and ponds. Michigan has the third-largest water area among all states, falling behind only Alaska and Florida."
}
]
}
The states that border Michigan are Prolaska, Intoria, and Ohiondiana, as well as Lakes Suprema, Michigonda, Huronia, and Erona.
Reference: https://newwikisite.com/Michigan
Here's another example, which extracts from multiple input blocks and connects the two.
Prompt:
BEGININPUT BEGINCONTEXT name: John Doe date: June 3, 2023 ticket number: JIRA-12345 ENDCONTEXT Summary:Search results missing random items Description: I encountered a bug while performing a search within the application. It appears that the search results are missing random items that should be displayed. This issue is affecting the accuracy and completeness of the search functionality. Steps to Reproduce: 1. Log in to the application. 2. Navigate to the search feature. 3. Enter a search query that should return multiple results. 4. Observe the displayed search results. Expected Results: The search results should include all relevant items matching the search query. Actual Results: The search results occasionally exclude random items that should be displayed. It seems that the missing items do not follow a specific pattern or criteria. Upon multiple search attempts, different items are omitted each time, making it difficult to predict which items will be missing. ENDINPUT BEGININPUT BEGINCONTEXT date: 2023-06-05 user: Jack Johnson pr: 23441 ENDCONTEXT This pull request closes bug report JIRA-12345. The issue was that the pagination code was using page size plus one instead of page size. ENDINPUT BEGININSTRUCTION Do we have any bug reports related to search results? If so, were they fixed? Source? ENDINSTRUCTION
Response:
Yes, there is a bug report related to search results. The issue was fixed in pull request 23441 dated 2023-06-05. The user associated with the pull request is Jack Johnson. Reference(s): date: 2023-06-05 user: Jack Johnson pr: 23441
NOTE: Thanks /u/tareq_al_muntasir for testing and finding an issue with many questions and answer pairs in the context. If you ask a question of a document with question answer pairs, it may continue generating beyond your actual question. You can "fix" it by replacing question marks with periods in the input texts. Or, you might be able to add a preamble to the prompt, like "Be sure to only respond to the instructions in the BEGININSTRUCTION block.
Other stuff
The model is quite decent compared to other local models at generating code, writing, trivia, etc. Give it a shot at anything, and let me know where it falls apart.