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Eric Hartford's WizardLM 13B V1.0 Uncensored GPTQ

These files are GPTQ 4bit model files for Eric Hartford's WizardLM 13B V1.0 Uncensored merged with Kaio Ken's SuperHOT 8K.

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

This is an experimental new GPTQ which offers up to 8K context size

The increased context is tested to work with ExLlama, via the latest release of text-generation-webui.

It has also been tested from Python code using AutoGPTQ, and trust_remote_code=True.

Code credits:

Original concept and code for increasing context length: kaiokendev
Updated Llama modelling code that includes this automatically via trust_remote_code: emozilla.

Please read carefully below to see how to use it.

GGML versions are not yet provided, as there is not yet support for SuperHOT in llama.cpp. This is being investigated and will hopefully come soon.

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 with ExLlama

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/WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ.
Click Download.
The model will start downloading. Once it's finished it will say "Done"
Untick Autoload the model
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded: WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ
To use the increased context, set the Loader to ExLlama, set max_seq_len to 8192 or 4096, and set compress_pos_emb to 4 for 8192 context, or to 2 for 4096 context.
Now click Save Settings followed by Reload
The model will automatically load, and is now ready for use!
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 with AutoGPTQ

First make sure you have AutoGPTQ and Einops installed:

pip3 install einops auto-gptq

Then run the following code. Note that in order to get this to work, config.json has been hardcoded to a sequence length of 8192.

If you want to try 4096 instead to reduce VRAM usage, please manually edit config.json to set max_position_embeddings to the value you want.

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse

model_name_or_path = "TheBloke/WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ"
model_basename = "wizardlm-13b-v1.0-uncensored-superhot-8k-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_map='auto',
        use_triton=use_triton,
        quantize_config=None)

model.seqlen = 8192

# 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'])

Using other UIs: monkey patch

Provided in the repo is llama_rope_scaled_monkey_patch.py, written by @kaiokendev.

It can be theoretically be added to any Python UI or custom code to enable the same result as trust_remote_code=True. I have not tested this, and it should be superseded by using trust_remote_code=True, but I include it for completeness and for interest.

Provided files

wizardlm-13b-v1.0-uncensored-superhot-8k-GPTQ-4bit-128g.no-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 with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.

wizardlm-13b-v1.0-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors
- Works for use with ExLlama with increased context (4096 or 8192)
- Works with AutoGPTQ in Python code, including with increased context, if trust_remote_code=True is set.
- Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. 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.

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.

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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: Kaio Ken's SuperHOT 8K

SuperHOT Prototype 2 w/ 8K Context

This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in the github blog. Tests have shown that the model does indeed leverage the extended context at 8K.

You will need to use either the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192

Looking for Merged & Quantized Models?

30B 4-bit CUDA: tmpupload/superhot-30b-8k-4bit-safetensors
30B 4-bit CUDA 128g: tmpupload/superhot-30b-8k-4bit-128g-safetensors

Training Details

I trained the LoRA with the following configuration:

1200 samples (~400 samples over 2048 sequence length)
learning rate of 3e-4
3 epochs
The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
Rank = 4
Alpha = 8
no dropout
weight decay of 0.1
AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
Trained on 4-bit base model

Original model card: Eric Hartford's WizardLM 13B V1.0 Uncensored

This is a retraining of https://huggingface.co/WizardLM/WizardLM-13B-V1.0 with a filtered dataset, intended to reduce refusals, avoidance, and bias.

Note that LLaMA itself has inherent ethical beliefs, so there's no such thing as a "truly uncensored" model. But this model will be more compliant than WizardLM/WizardLM-7B-V1.0.

Shout out to the open source AI/ML community, and everyone who helped me out.

Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

Like WizardLM/WizardLM-13B-V1.0, this model is trained with Vicuna-1.1 style prompts.

You are a helpful AI assistant.

USER: <prompt>
ASSISTANT:

Thank you chirper.ai for sponsoring some of my compute!