初始化项目，由ModelHub XC社区提供模型
Model: NickIBrody/qwen-linux Source: Original Platform
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README.md
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README.md
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---
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language:
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- ru
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- en
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license: apache-2.0
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tags:
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- linux
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- shell
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- qwen2
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- unsloth
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- gguf
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base_model: Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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---
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# Qwen2.5-3B Linux Assistant
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A fine-tuned version of [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) trained to act as a Linux/Shell command assistant. Given a natural language description, the model outputs the correct shell command.
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Supports both **Russian** and **English** input.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | Qwen2.5-3B-Instruct |
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| Fine-tuning method | QLoRA (LoRA r=16, alpha=16) |
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| Training steps | ~1700 |
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| Epochs | 3 |
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| Final loss | ~0.28 |
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| Dataset size | ~4500 examples |
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| Languages | Russian, English |
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| Framework | Unsloth + TRL |
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---
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## Usage
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### Ollama (recommended)
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```bash
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ollama run hf.co/NickIBrody/qwen-linux-gguf
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```
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### llama.cpp
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```bash
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llama-cli -hf NickIBrody/qwen-linux-gguf --jinja
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```
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### Python (transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "NickIBrody/qwen-linux"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
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messages = [
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{"role": "system", "content": "You are a Linux assistant. Reply only with the shell command, no explanations."},
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{"role": "user", "content": "show top 5 processes by memory usage"},
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]
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inp = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(inp, max_new_tokens=128, temperature=0.3)
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print(tok.decode(out[0][inp.shape[1]:], skip_special_tokens=True))
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```
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---
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## Examples
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| Input | Output |
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|---|---|
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| показажи топ 5 процессов по памяти | `ps aux --sort=-%mem \| head -n 5` |
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| где я нахожусь в терминале | `pwd` |
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| compress file data.txt with bzip2 | `bzip2 data.txt` |
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| show disk usage in human readable format | `df -h` |
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| find all .log files modified in last 7 days | `find / -name "*.log" -mtime -7` |
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| kill process by name nginx | `pkill nginx` |
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| show open ports | `ss -tulnp` |
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---
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## Dataset
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Training data: [NickIBrody/linux-commands-ru-en](https://huggingface.co/datasets/NickIBrody/linux-commands-ru-en)
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~4500 shell command examples in Russian and English, covering:
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- File system navigation and management
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- Process management
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- Networking
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- Archive and compression
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- System monitoring
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- Package management
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---
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## Training Code
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```python
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from unsloth import FastLanguageModel
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from unsloth.chat_templates import get_chat_template
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from datasets import load_dataset
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from trl import SFTTrainer
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from transformers import TrainingArguments
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model, tok = FastLanguageModel.from_pretrained(
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"unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
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max_seq_length=2048,
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load_in_4bit=True
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)
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model = FastLanguageModel.get_peft_model(
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model, r=16, lora_alpha=16,
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target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
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)
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tok = get_chat_template(tok, chat_template="qwen-2.5")
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ds = load_dataset("NickIBrody/linux-commands-ru-en", split="train")
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ds = ds.map(lambda x: {"text": tok.apply_chat_template(x["messages"], tokenize=False)})
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SFTTrainer(
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model=model,
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tokenizer=tok,
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train_dataset=ds,
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dataset_text_field="text",
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max_seq_length=2048,
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args=TrainingArguments(
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per_device_train_batch_size=2,
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gradient_accumulation_steps=4,
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num_train_epochs=3,
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learning_rate=2e-4,
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fp16=True,
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logging_steps=10,
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output_dir="out",
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optim="adamw_8bit"
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)
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).train()
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model.save_pretrained_gguf("qwen-linux", tok, quantization_method="q4_k_m")
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```
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---
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## Limitations
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- Designed for shell commands only, not general conversation
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- May struggle with highly complex multi-step scripts
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- Best results with clear, specific prompts
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---
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## License
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Apache 2.0
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