Update README.md

README.md

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+# Qwen2.5-3B Linux Assistant
+
+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.
+
+Supports both **Russian** and **English** input.
+
 ---
-tags:
-- gguf
-- llama.cpp
-- unsloth
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base model | Qwen2.5-3B-Instruct |
+| Fine-tuning method | QLoRA (LoRA r=16, alpha=16) |
+| Training steps | ~1700 |
+| Epochs | 3 |
+| Final loss | ~0.28 |
+| Dataset size | ~4500 examples |
+| Languages | Russian, English |
+| Framework | Unsloth + TRL |
+
+---
+
+## Usage
+
+### Ollama (recommended)
+```bash
+ollama run hf.co/NickIBrody/qwen-linux-gguf
+```
+
+### llama.cpp
+```bash
+llama-cli -hf NickIBrody/qwen-linux-gguf --jinja
+```
+
+### Python (transformers)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "NickIBrody/qwen-linux"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a Linux assistant. Reply only with the shell command, no explanations."},
+    {"role": "user", "content": "show top 5 processes by memory usage"},
+]
+inp = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inp, max_new_tokens=128, temperature=0.3)
+print(tok.decode(out[0][inp.shape[1]:], skip_special_tokens=True))
+```
+
+---
+
+## Examples
+
+| Input | Output |
+|---|---|
+| показажи топ 5 процессов по памяти | `ps aux --sort=-%mem \| head -n 5` |
+| где я нахожусь в терминале | `pwd` |
+| compress file data.txt with bzip2 | `bzip2 data.txt` |
+| show disk usage in human readable format | `df -h` |
+| find all .log files modified in last 7 days | `find / -name "*.log" -mtime -7` |
+| kill process by name nginx | `pkill nginx` |
+| show open ports | `ss -tulnp` |
 
 ---
 
-# qwen-linux-gguf : GGUF
+## Dataset
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+Training data: [NickIBrody/linux-commands-ru-en](https://huggingface.co/datasets/NickIBrody/linux-commands-ru-en)
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf NickIBrody/qwen-linux-gguf --jinja`
-- For multimodal models: `llama-mtmd-cli -hf NickIBrody/qwen-linux-gguf --jinja`
+~4500 shell command examples in Russian and English, covering:
+- File system navigation and management
+- Process management
+- Networking
+- Archive and compression
+- System monitoring
+- Package management
+
+---
+
+## Training Code
+
+```python
+from unsloth import FastLanguageModel
+from unsloth.chat_templates import get_chat_template
+from datasets import load_dataset
+from trl import SFTTrainer
+from transformers import TrainingArguments
+
+model, tok = FastLanguageModel.from_pretrained(
+    "unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
+    max_seq_length=2048,
+    load_in_4bit=True
+)
+
+model = FastLanguageModel.get_peft_model(
+    model, r=16, lora_alpha=16,
+    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
+)
+
+tok = get_chat_template(tok, chat_template="qwen-2.5")
+
+ds = load_dataset("NickIBrody/linux-commands-ru-en", split="train")
+ds = ds.map(lambda x: {"text": tok.apply_chat_template(x["messages"], tokenize=False)})
+
+SFTTrainer(
+    model=model,
+    tokenizer=tok,
+    train_dataset=ds,
+    dataset_text_field="text",
+    max_seq_length=2048,
+    args=TrainingArguments(
+        per_device_train_batch_size=2,
+        gradient_accumulation_steps=4,
+        num_train_epochs=3,
+        learning_rate=2e-4,
+        fp16=True,
+        logging_steps=10,
+        output_dir="out",
+        optim="adamw_8bit"
+    )
+).train()
+
+model.save_pretrained_gguf("qwen-linux", tok, quantization_method="q4_k_m")
+```
+
+---
+
+## Limitations
+
+- Designed for shell commands only, not general conversation
+- May struggle with highly complex multi-step scripts
+- Best results with clear, specific prompts
+
+---
 
-## Available Model files:
-- `Qwen2.5-3B-Instruct.Q4_K_M.gguf`
+## License
 
-## Ollama
-An Ollama Modelfile is included for easy deployment.
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+Apache 2.0