🚀 Tessa-Rust-T1,专注于Rust的代码生成模型
Tessa-Rust-T1是一款基于Transformer架构的Rust代码生成模型,它以强大的Qwen2.5-Coder-7B-Instruct为基础模型进行微调。该模型专为Rust开发设计,能利用先进的推理能力自主生成结构良好、符合习惯用法的Rust代码,可集成到代理系统中,助力后端开发、系统编程等工作。
🚀 快速开始
推理示例
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "tesslate/Tessa-Rust-T1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
prompt = """<|im_start|>user
Create a Rust function using the `rayon` crate to parallelize summing a vector of integers.
Function signature: `fn parallel_sum(data: &[i32]) -> i32`
<|im_end|>
<|im_start|>assistant
<|im_start|>think
"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=500, do_sample=True, temperature=0.6, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
✨ 主要特性
Your role as a Rust assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <|begin_of_thought|> {thought process with each logical step separated by '\n\n'} <|end_of_thought|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <|begin_of_solution|> Provide all the code necessary to solve the problem in the same code block. <|end_of_solution|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines:
- 特定于Rust的推理:能够准确生成功能完善且符合习惯用法的Rust代码。
- 代理集成:可无缝融入由AI驱动的编码代理和自主开发系统。
- 上下文感知生成:能有效理解并利用Rust项目上下文、依赖项(crates)和语言特性(生命周期、借用、特性),提供相关的代码解决方案。
📦 安装指南
文档未提及安装相关内容,暂无法提供安装指南。
💻 使用示例
基础用法
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "tesslate/Tessa-Rust-T1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
prompt = """<|im_start|>user
Create a Rust function using the `rayon` crate to parallelize summing a vector of integers.
Function signature: `fn parallel_sum(data: &[i32]) -> i32`
<|im_end|>
<|im_start|>assistant
<|im_start|>think
"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=500, do_sample=True, temperature=0.6, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
📚 详细文档
使用场景
推荐用途
- 自动生成Rust代码:根据文本提示快速生成Rust函数、结构体、模块和样板代码。
- 基于代理的Rust开发:集成到自动化编码系统中,加快后端、系统或工具的开发流程。
- Rust代码重构:自动优化和改进Rust代码,使其更符合习惯用法并提高性能。
- 生成命令行工具:加速命令行应用程序的创建。
- 实现API端点:通过生成路由处理程序和数据模型,加快后端开发。
- 编写单元测试:为Rust函数和模块生成测试用例。
局限性
- 专注于Rust:在Rust生态系统之外的用途有限。
- 复杂逻辑/生命周期:对于高度复杂的异步模式、复杂的生命周期管理或大量的
unsafe
代码块,可能需要手动调整。
- 构建配置:可能无法完全自动化
Cargo.toml
管理或复杂的构建脚本。
性能和评估
- 优点:
- 能够生成高质量、符合习惯用法的Rust代码。
- 与基于代理的系统具有出色的集成能力。
- 理解常见的Rust模式和标准库的使用。
- 缺点:
- 对于复杂的Rust逻辑(如高级泛型、宏、复杂的生命周期、
unsafe
代码),可能需要手动后处理或优化。
- 对于不太常见的库,可能会虚构不存在的crate特性或错误的API使用。
技术规格
属性 |
详情 |
模型类型 |
基于Transformer的大语言模型 |
基础模型 |
Qwen2.5-Coder-7B-Instruct |
精度 |
bf16混合精度(根据最终模型发布情况,可能提供q8等量化选项) |
硬件要求 |
建议使用具有12GB以上VRAM的设备(量化情况不同,要求可能会有所变化) |
软件依赖 |
Hugging Face Transformers (transformers>=4.34 )、PyTorch (torch>=2.0 )、Accelerate (accelerate ) 用于优化加载/推理 |
引用
@misc{tesslate_Tessa-Rust-T1, # Adjusted name
title={Tessa-Rust-T1: A Rust-Focused Code Generation Model},
author={tesslate},
year={2025}, # Placeholder year
publisher={Hugging Face},
url={https://huggingface.co/tesslate/Tessa-7B}
}
支持与交流
赞助者
合作者
由Ravi、Ernest和Tesslate团队完成。
联系我们
🔧 技术细节
文档未提及技术实现细节相关内容,暂无法提供技术细节。
📄 许可证
本模型使用的许可证为:apache-2.0。