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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))