README.md

---
base_model: https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B
inference: false
license: other
model_creator: CodeFuse AI
model_name: CodeFuse CodeLlama 34B
model_type: llama
prompt_template: '<|role_start|>system<|role_end|>{system_message}

  <|role_start|>human<|role_end|>{prompt}

  <|role_start|>bot<|role_end|>

  '
quantized_by: TheBloke
tasks:
- code-generation
---

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# CodeFuse CodeLlama 34B - GPTQ
- Model creator: [CodeFuse AI](https://huggingface.co/codefuse-ai)
- Original model: [CodeFuse CodeLlama 34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B)

<!-- description start -->
## Description

This repo contains GPTQ model files for [CodeFuse AI's CodeFuse CodeLlama 34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B).

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

<!-- description end -->
<!-- repositories-available start -->
## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GGUF)
* [CodeFuse AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: CodeFuse

```
<|role_start|>system<|role_end|>{system_message}
<|role_start|>human<|role_end|>{prompt}
<|role_start|>bot<|role_end|>

```

<!-- prompt-template end -->
<!-- licensing start -->
## Licensing

The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [CodeFuse AI's CodeFuse CodeLlama 34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B).
<!-- licensing end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch.  See below for instructions on fetching from different branches.

All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.

<details>
  <summary>Explanation of GPTQ parameters</summary>

- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used.  Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.

</details>

| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 17.69 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | 
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 20.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | 
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 18.33 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 13.54 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | 
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 14.14 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | 
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 34.30 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |

<!-- README_GPTQ.md-provided-files end -->

<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches

- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeFuse-CodeLlama-34B-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/CodeFuse-CodeLlama-34B-GPTQ`.
  - To download from a specific branch, enter for example `TheBloke/CodeFuse-CodeLlama-34B-GPTQ:main`
  - see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `CodeFuse-CodeLlama-34B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. 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`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->

<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code

### Install the necessary packages

Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7
```

If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```

### For CodeLlama models only: you must use Transformers 4.33.0 or later.

If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```

### You can then use the following code

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/CodeFuse-CodeLlama-34B-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''<|role_start|>system<|role_end|>{system_message}
<|role_start|>human<|role_end|>{prompt}
<|role_start|>bot<|role_end|>

'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->

<!-- README_GPTQ.md-compatibility start -->
## Compatibility

The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).

[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->

<!-- footer start -->
<!-- 200823 -->
## 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!

Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

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**: Aemon Algiz.

**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

<!-- footer end -->

# Original model card: CodeFuse AI's CodeFuse CodeLlama 34B

# Model Card for CodeFuse-CodeLlama-34B
![logo](LOGO.png)

[[中文]](#chinese)    [[English]](#english)



<a id="english"></a>

## Model Description

CodeFuse-CodeLlama-34B is a 34B Code-LLM finetuned by QLoRA of multiple code tasks（600k instrunctions/answers） on the base model CodeLlama-34b-Python. 
The context length of finetuning is 4K while it is able to be finetuned by 16k context if necessary.
<br>

## News and Updates

🔥🔥🔥 CodeFuse-CodeLlama34B-MFT has achived 74.4% of pass@1 on HumanEval, which is SOTA at present.

<br>

## Code Community

**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)

+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨

+ If you wish to deploy the model yourself, you can visit ✨[FasterTransformer4CodeFuse](https://github.com/codefuse-ai/FasterTransformer4CodeFuse)✨✨

+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨


## Performance


| Model                       | HumanEval(pass@1) |  Date   |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B**  |     **74.4%**      | 2023.9  |
| WizardCoder-Python-34B-V1.0 |       73.2%       | 2023.8  |
| GPT-4(zero-shot)            |       67.0%       | 2023.3  |
| PanGu-Coder2 15B            |       61.6%       | 2023.8  |
| CodeLlama-34b-Python        |       53.7%       | 2023.8  |
| CodeLlama-34b               |       48.8%       | 2023.8  |
| GPT-3.5(zero-shot)          |       48.1%       | 2022.11 |
| OctoCoder                   |       46.2%       | 2023.8  |
| StarCoder-15B               |       33.6%       | 2023.5  |
| LLaMA 2 70B(zero-shot)      |       29.9%       | 2023.7  |

<br>

## Requirements

* python>=3.8 
* pytorch>=2.0.0
* transformers==4.32.0
* Sentencepiece
* CUDA 11.4
  <br>

##  Inference String Format

The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format.  It is used as input during the inference process.
Here is an example format of the concatenated string:

```python
"""
<|role_start|>system<|role_end|>System instruction
<|role_start|>human<|role_end|>Human 1st round input
<|role_start|>bot<|role_end|>Bot 1st round output</s>
<|role_start|>human<|role_end|>Human 2nd round input
<|role_start|>bot<|role_end|>Bot 2nd round output</s>
...
...
...
<|role_start|>human<|role_end|>Human nth round input
<|role_start|>bot<|role_end|>{Bot output to be genreated}</s>
"""
```

When applying inference, you always make your input string end with "<|role_start|>bot<|role_end|>" to ask the model generating answers.

## Quickstart

```bash
pip install -r requirements.txt
```

```python
import torch
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM,
)
tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True, use_fast=False, legacy=False)
tokenizer.padding_side = "left"
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<unk>")
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("</s>")
# try 4bit loading if cuda memory not enough
model = AutoModelForCausalLM.from_pretrained(mode_name_or_path,
                                             trust_remote_code=True,
                                             load_in_4bit=False,
                                             device_map="auto",
                                             torch_dtype=torch.bfloat16)
model.eval()

HUMAN_ROLE_START_TAG = "<|role_start|>human<|role_end|>"
BOT_ROLE_START_TAG = "<|role_start|>bot<|role_end|>"

text = f"{HUMAN_ROLE_START_TAG}write a python function of quick sort.{BOT_ROLE_START_TAG}" 
inputs = tokenizer(text, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda")
outputs = model.generate(
        inputs=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=512,
        top_p=0.95,
        temperature=0.1,
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id
    )
gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(gen_text)
```

## MD5
We notice that the file may be corrupted during transfer process. Please check MD5 value before use.

| Model File                       | MD5 Value                        |
|:---------------------------------|:--------------------------------:|
| pytorch_model-00001-of-00007.bin | 8d544b1bcb3449934184d4141137329c |
| pytorch_model-00002-of-00007.bin | 9d5dbb30911e48a42fb6d0fcabb322a4 |
| pytorch_model-00003-of-00007.bin | b0d4aecee0457d9332005a187e1fffed |
| pytorch_model-00004-of-00007.bin | 5c7e002de5eab77d0194a2b0f6de0c24 |
| pytorch_model-00005-of-00007.bin | d22a511aa26b5b17117b665a877490ab |
| pytorch_model-00006-of-00007.bin | a5c28ac277fac07d16dd66537e54d109 |
| pytorch_model-00007-of-00007.bin | a967e2c6195477b7407089c0bffa2d53 |


<a id="chinese"></a>

## 模型简介

CodeFuse-CodeLlama34B-MFT 是一个通过QLoRA对基座模型CodeLlama-34b-Python进行多代码任务微调的代码大模型。模型微调采用了4k上下文。如果有必要，可以扩展到16k。
<br>

## 新闻

🔥🔥🔥 CodeFuse-CodeLlama34B-MFT模型在HumanEval pass@1上可以达到74.4%, 为当前开源SOTA。

<br>

## 代码社区
**大本营**： 🏡 https://github.com/codefuse-ai （**欢迎为我们的项目一键三连 Star🌟 + Fork🚀 + Watch👀**）

+ 如果您想自己微调该模型，可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨

+ 如果您想自己部署该模型，可以访问 ✨[FasterTransformer4CodeFuse](https://github.com/codefuse-ai/FasterTransformer4CodeFuse)✨✨

+ 如果您想观看该模型示例，可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨


## 评测表现(代码)

| 模型                          | HumanEval(pass@1) |   日期    |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B**  |     **74.4%**      | 2023.9  |
| WizardCoder-Python-34B-V1.0 |       73.2%       | 2023.8  |
| GPT-4(zero-shot)            |       67.0%       | 2023.3  |
| PanGu-Coder2 15B            |       61.6%       | 2023.8  |
| CodeLlama-34b-Python        |       53.7%       | 2023.8  |
| CodeLlama-34b               |       48.8%       | 2023.8  |
| GPT-3.5(zero-shot)          |       48.1%       | 2022.11 |
| OctoCoder                   |       46.2%       | 2023.8  |
| StarCoder-15B               |       33.6%       | 2023.5  |
| LLaMA 2 70B(zero-shot)      |       29.9%       | 2023.7  |

<br>

## Requirements

* python>=3.8 
* pytorch>=2.0.0
* transformers==4.32.0
* CUDA 11.4
<br>

## 推理数据格式

推理数据为模型在训练数据格式下拼接的字符串形式，它也是推理时输入prompt拼接的方式：

```python
"""
<|role_start|>system<|role_end|>这是System指令
<|role_start|>human<|role_end|>这是第1轮用户输入的问题
<|role_start|>bot<|role_end|>这是第1轮模型生成的内容</s>
<|role_start|>human<|role_end|>这是第2轮用户输入的问题
<|role_start|>bot<|role_end|>这是第2轮模型生成的内容</s>
...
...
...
<|role_start|>human<|role_end|>这是第n轮用户输入的问题
<|role_start|>bot<|role_end|>{模型现在要生成的内容}</s>
"""
```

推理时，请确保拼接的prompt字符串以"<|role_start|>bot<|role_end|>"结尾，引导模型生成回答。

## 快速使用

```python
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM,
)
tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True, use_fast=False, legacy=False)
tokenizer.padding_side = "left"
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<unk>")
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("</s>")
# 如果显存不够，可以考虑量化加载
model = AutoModelForCausalLM.from_pretrained(mode_name_or_path,
                                             trust_remote_code=True,
                                             load_in_4bit=False,
                                             device_map="auto",
                                             torch_dtype=torch.bfloat16)
model.eval()

HUMAN_ROLE_START_TAG = "<|role_start|>human<|role_end|>"
BOT_ROLE_START_TAG = "<|role_start|>bot<|role_end|>"

text = f"{HUMAN_ROLE_START_TAG}请用C++实现求解第n个斐波那契数{BOT_ROLE_START_TAG}" 
inputs = tokenizer(text, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda")
outputs = model.generate(
        inputs=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=512,
        top_p=0.95,
        temperature=0.1,
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id
    )
gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(gen_text)
```


## MD5
我们发现模型文件可能会在传输过程中损坏，使用前请检查文件MD5值。

| 模型文件                           | MD5值                            |
|:---------------------------------|:--------------------------------:|
| pytorch_model-00001-of-00007.bin | 8d544b1bcb3449934184d4141137329c |
| pytorch_model-00002-of-00007.bin | 9d5dbb30911e48a42fb6d0fcabb322a4 |
| pytorch_model-00003-of-00007.bin | b0d4aecee0457d9332005a187e1fffed |
| pytorch_model-00004-of-00007.bin | 5c7e002de5eab77d0194a2b0f6de0c24 |
| pytorch_model-00005-of-00007.bin | d22a511aa26b5b17117b665a877490ab |
| pytorch_model-00006-of-00007.bin | a5c28ac277fac07d16dd66537e54d109 |
| pytorch_model-00007-of-00007.bin | a967e2c6195477b7407089c0bffa2d53 |