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LoupGarou's WizardCoder Guanaco 15B V1.0 GPTQ

这些文件是用于 LoupGarou's WizardCoder Guanaco 15B V1.0 的GPTQ 4位模型文件。

这是使用 GPTQ-for-LLaMa 进行4位量化的结果。

可用的存储库

• 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

提示模板：Alpaca

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

### Instruction: PROMPT

### Response:

如何在 text-generation-webui 中轻松下载和使用此模型

请确保您正在使用最新版本的 text-generation-webui 。

强烈推荐使用一键安装程序进行文本生成WebUI，除非您知道如何进行手动安装。

• 请注意，您不再需要设置GPTQ参数。这些参数将自动从文件 quantize_config.json 中设置。

如何在Python代码中使用此GPTQ模型

首先确保您已经安装了 AutoGPTQ ：

GITHUB_ACTIONS=true pip install auto-gptq

然后尝试以下示例代码：

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]))

也可以使用transformers的pipeline进行推理

使用AutoGPTQ的pipeline时防止打印不必要的transformers错误

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.

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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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Thank you to all my generous patrons and donaters!

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# 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()

训练过程

基于openassistant-guanaco数据集使用QLORA对基础WizardCoder模型进行微调，该数据集在问题集的令牌大小的2个标准差之内修剪并进行了随机化。此微调数据集还删除了所有非英语数据。

致谢

这个模型WizardCoder-Guanaco-15B-V1.0，是在两个优秀团队的努力的基础上构建的，以评估结合了 WizardCoder base model 和 openassistant-guanaco dataset 的模型的性能。

衷心感谢开发人员和参与创建和完善这些模型的社区。他们在提供开源工具和数据集方面的承诺对于使这个项目成为现实至关重要。

此外，特别感谢 Hugging Face 团队，他们的革命性库不仅简化了模型创建和适应的过程，还使得每个人都能够接触到最先进的机器学习技术。他们对该项目的发展产生的影响不言而喻。