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Jon Durbin's Airoboros 65B GPT4 1.3 GPTQ

These files are GPTQ 4bit model files for Jon Durbin's Airoboros 65B GPT4 1.3.

It is the result of quantising to 4bit using GPTQ-for-LLaMa.

Note from model creator Jon Durbin: This version has problems, use if you dare, or wait for 1.4.

Repositories available

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

Click the Model tab.
Under Download custom model or LoRA, enter TheBloke/airoboros-65B-gpt4-1.3-GPTQ.
Click Download.
The model will start downloading. Once it's finished it will say "Done"
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded: airoboros-65B-gpt4-1.3-GPTQ
The model will automatically load, and is now ready for use!
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 and should not set manual GPTQ parameters any more. These are set automatically from the file quantize_config.json.

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 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-65B-gpt4-1.3-GPTQ"
model_basename = "airoboros-65b-gpt4-1.3-GPTQ-4bit--1g.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=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

# Note: check the prompt template is correct for this model.
prompt = "Tell me about AI"
prompt_template=f'''USER: {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

airoboros-65b-gpt4-1.3-GPTQ-4bit--1g.act.order.safetensors

This will work with AutoGPTQ, ExLlama, 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.

airoboros-65b-gpt4-1.3-GPTQ-4bit--1g.act.order.safetensors
- Works with AutoGPTQ in CUDA or Triton modes.
- LLaMa models also work with [ExLlama](https://github.com/turboderp/exllama}, which usually provides much higher performance, and uses less VRAM, than AutoGPTQ.
- 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:

TheBloke AI's Discord server

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.

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Special thanks to: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Jon Durbin's Airoboros 65B GPT4 1.3

This version has problems, use if you dare, or wait for 1.4.

Overview

This is a qlora fine-tuned 65b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros

This is mostly an extension of 1.2 with a few enhancements:

All coding instructions have an equivalent " PLAINFORMAT" version now.
Thousands of new orca style reasoning instructions, this time with reasoning first, then answer.
Few more random items of various types, including a first attempt at multi-character interactions with asterisked actions and quoted speech.

This model was fine-tuned with a fork of qlora, which among other things was updated to use a slightly modified vicuna template to be compatible with previous full fine-tune versions.

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).

Usage

To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a --no-history option to prevent input tokenization errors.

pip install git+https://github.com/jondurbin/FastChat

Be sure you are pulling the latest branch!

Then, you can invoke it like so (after downloading the model):

python -m fastchat.serve.cli \
  --model-path airoboros-65b-gpt4-1.3 \
  --temperature 0.5 \
  --max-new-tokens 2048 \
  --no-history

Training details

Fine-tuned with my fork of qlora: https://github.com/jondurbin/qlora

Using:

export WANDB_PROJECT=airoboros-65b-gpt4-1.3

python qlora.py \
    --model_name_or_path ./llama-65b-hf \
    --output_dir ./airoboros-65b-gpt4-1.3-peft \
    --max_steps 2520 \
    --logging_steps 1 \
    --save_strategy steps \
    --data_seed 11422 \
    --save_steps 75 \
    --save_total_limit 3 \
    --evaluation_strategy "no" \
    --eval_dataset_size 2 \
    --max_new_tokens 2800 \
    --dataloader_num_workers 3 \
    --logging_strategy steps \
    --remove_unused_columns False \
    --do_train \
    --lora_r 64 \
    --lora_alpha 16 \
    --lora_modules all \
    --double_quant \
    --quant_type nf4 \
    --bf16 \
    --bits 4 \
    --warmup_ratio 0.03 \
    --lr_scheduler_type constant \
    --gradient_checkpointing \
    --dataset instructions.jsonl \
    --dataset_format airoboros \
    --model_max_len 2800 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 16 \
    --learning_rate 0.0001 \
    --adam_beta2 0.999 \
    --max_grad_norm 0.3 \
    --lora_dropout 0.05 \
    --weight_decay 0.0 \
    --seed 11422 \
    --report_to wandb

Three file modifications to the base llama:

llama-65b-hf/tokenizer_config.json (see this repo's version, updated to have 4096 max seq length during training to accomodate training data)
llama-65b-hf/special_tokens_map.json (see this repo's version)
llama-65b-hf/config.json (updated to temporarily have max model size 4096 to accomodate training data)

Afterwards, the changes to max model length and sequence length are reduced back to 2048 to avoid ... issues ...

TheBloke
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airoboros-65B-gpt4-1.3-GPTQ