Create README.MD

README.MD

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-Coder-1.5B
+tags:
+- causal-lm
+- qwen
+- qwen3
+- code
+- coder
+- lora-merged
+- code-analysis
+pipeline_tag: text-generation
+---
+
+# Code_analyze_1.0
+
+**Code_analyze_1.0** is a merged LoRA fine-tuned version of **Qwen3-Coder-1.5B**, optimized for
+code analysis, code understanding, and reasoning over source code.
+
+## Model Details
+
+- **Base model:** Qwen/Qwen3-Coder-1.5B  
+- **Model type:** Causal Language Model  
+- **Fine-tuning method:** LoRA (merged into base weights)  
+- **Languages:** Primarily English (code-focused), supports multilingual comments  
+- **Domain:** Programming / Software Engineering  
+
+This model is **fully merged and standalone** — no additional LoRA adapters or base model
+dependencies are required at inference time.
+
+## Intended Use
+
+The model is designed for:
+- Static code analysis
+- Bug detection and explanation
+- Code review and refactoring suggestions
+- Understanding unfamiliar codebases
+- Explaining algorithms and logic in source code
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Vilyam888/Code_analyze_1.0"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    device_map="auto"
+)
+
+prompt = "Analyze this Python function and find potential issues:\n\n```python\ndef f(x): return x + 1\n```"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))