Chat & support: my new Discord server

Want to contribute? TheBloke's Patreon page

LoupGarou's WizardCoder Guanaco 15B V1.0 GPTQ

These files are GPTQ 4bit model files for LoupGarou's WizardCoder Guanaco 15B V1.0 .

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

Repositories available

• 4-bit GPTQ models for GPU inference
• 4, 5, and 8-bit GGML models for CPU+GPU inference
• Unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: Alpaca

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

### Instruction: PROMPT

### Response:

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 know how to make a manual install.

• 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:

GITHUB_ACTIONS=true 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/WizardCoder-Guanaco-15B-V1.0-GPTQ"
model_basename = "wizardcoder-guanaco-15b-v1.0-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=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

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:

'''

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

**wizardcoder-guanaco-15b-v1.0-GPTQ-4bit-128g.no-act.order.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.

As this is not a Llama model, it will not be supported by ExLlama.

It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.

* `wizardcoder-guanaco-15b-v1.0-GPTQ-4bit-128g.no-act.order.safetensors`
  * Works with AutoGPTQ in CUDA or Triton modes.
  * Does NOT work with [ExLlama](https://github.com/turboderp/exllama).
  * Untested with GPTQ-for-LLaMa.
  * Works with text-generation-webui, including one-click-installers.
  * Parameters: Groupsize = 128. Act Order / desc_act = False.

<!-- footer start -->
## 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!

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.

**Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi.

Thank you to all my generous patrons and donaters!

<!-- footer end -->

# Original model card: LoupGarou's WizardCoder Guanaco 15B V1.0


## WizardGuanaco-V1.0 Model Card
The WizardCoder-Guanaco-15B-V1.0 is a language model that combines the strengths of the [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) base model and the [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset for finetuning. The openassistant-guanaco dataset was further trimmed to within 2 standard deviations of token size for input and output pairs and all non-english data has been removed to reduce training size requirements.

# Model Description
This model is built on top of the WizardCoder base model, a large language model known for its impressive capabilities in code related instruction. The WizardCoder base model was further finetuned using QLORA on the openassistant-guanaco dataset to enhance its generative abilities.

However, to ensure more targeted learning and data processing, the dataset was trimmed to within 2 standard deviations of token size for question sets. This process enhances the model's ability to generate more precise and relevant answers, eliminating outliers that could potentially distort the responses. In addition, to focus on English language proficiency, all non-English data has been removed from the Guanaco dataset.

# Intended Use
This model is designed to be used for a wide array of text generation tasks that require understanding and generating English text. The model is expected to perform well in tasks such as answering questions, writing essays, summarizing text, translation, and more. However, given the specific data processing and finetuning done, it might be particularly effective for tasks related to English language question-answering systems.

# Limitations
Despite the powerful capabilities of this model, users should be aware of its limitations. The model's knowledge is up to date only until the time it was trained, and it doesn't know about events in the world after that. It can sometimes produce incorrect or nonsensical responses, as it doesn't understand the text in the same way humans do. It should be used as a tool to assist in generating text and not as a sole source of truth.

# How to use
Here is an example of how to use this model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import torch

class Chatbot:
    def __init__(self, model_name):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
        self.model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, torch_dtype=torch.bfloat16)
        if self.tokenizer.pad_token_id is None:
            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id

    def get_response(self, prompt):
        inputs = self.tokenizer.encode_plus(prompt, return_tensors="pt", padding='max_length', max_length=100)
        if next(self.model.parameters()).is_cuda:
            inputs = {name: tensor.to('cuda') for name, tensor in inputs.items()}
        start_time = time.time()
        tokens = self.model.generate(input_ids=inputs['input_ids'], 
                                    attention_mask=inputs['attention_mask'],
                                    pad_token_id=self.tokenizer.pad_token_id,
                                    max_new_tokens=400)
        end_time = time.time()
        output_tokens = tokens[0][inputs['input_ids'].shape[-1]:]
        output = self.tokenizer.decode(output_tokens, skip_special_tokens=True)
        time_taken = end_time - start_time
        return output, time_taken

def main():
    chatbot = Chatbot("LoupGarou/WizardCoder-Guanaco-15B-V1.0")
    while True:
        user_input = input("Enter your prompt: ")
        if user_input.lower() == 'quit':
            break
        output, time_taken = chatbot.get_response(user_input)
        print("\033[33m" + output + "\033[0m")
        print("Time taken to process: ", time_taken, "seconds")
    print("Exited the program.")

if __name__ == "__main__":
    main()

Training Procedure

The base WizardCoder model was finetuned on the openassistant-guanaco dataset using QLORA, which was trimmed to within 2 standard deviations of token size for question sets and randomized. All non-English data was also removed from this finetuning dataset.

Acknowledgements

This model, WizardCoder-Guanaco-15B-V1.0, is simply building on the efforts of two great teams to evaluate the performance of a combined model with the strengths of the WizardCoder base model and the openassistant-guanaco dataset .

A sincere appreciation goes out to the developers and the community involved in the creation and refinement of these models. Their commitment to providing open source tools and datasets have been instrumental in making this project a reality.

Moreover, a special note of thanks to the Hugging Face team, whose transformative library has not only streamlined the process of model creation and adaptation, but also democratized the access to state-of-the-art machine learning technologies. Their impact on the development of this project cannot be overstated.