CodeLens-7B

A fine-tuned Qwen2.5-7B-Instruct model specialized for code review, bug detection, and programming assistance. It analyzes code snippets, identifies issues, suggests improvements, and writes clean solutions across multiple programming languages.

Key Details

Dataset

Trained on 10,000 examples from sahil2801/CodeAlpaca-20k, a code instruction-following dataset covering code generation, debugging, explanation, and review tasks across Python, JavaScript, Java, C, SQL, and more.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("sriksven/CodeLens-7B")
tokenizer = AutoTokenizer.from_pretrained("sriksven/CodeLens-7B")

messages = [
 {
 "role": "system",
 "content": "You are an expert code reviewer and programmer. Analyze code, find bugs, suggest improvements, and write clean efficient solutions.",
 },
 {
 "role": "user",
 "content": "Review this Python function for bugs and improvements:\n\ndef find_duplicates(lst):\n seen = []\n dupes = []\n for i in lst:\n if i in seen:\n dupes.append(i)\n seen.append(i)\n return dupes",
 },
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Unsloth (faster inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
 model_name="sriksven/CodeLens-7B",
 max_seq_length=2048,
 load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Capabilities

• Code review — analyze code for bugs, anti-patterns, and style issues
• Bug detection — identify logical errors, off-by-one mistakes, edge cases
• Code generation — write functions, classes, and scripts from descriptions
• Code explanation — explain what a piece of code does step by step
• Refactoring suggestions — propose cleaner, more efficient alternatives
• Multi-language — Python, JavaScript, Java, C/C++, SQL, HTML/CSS, and more

Intended Use

• Local code review assistant
• Programming tutoring and education
• Code quality tooling in CI/CD pipelines
• Prototyping developer tools with local LLMs

Limitations

• Trained on instruction-following code data, not real code review conversations from PRs
• May not catch security vulnerabilities that require deep context
• Code suggestions should be tested before use in production
• Best with shorter code snippets (functions/classes) rather than full files
• No execution or testing capability — suggestions are pattern-based

Training Metrics

Loss decreased steadily from 2.17 to 0.27 over 500 steps (~13 epochs), indicating strong learning on the code instruction data.

Training Infrastructure

Source Code

Training scripts: github.com/sriksven/LLM-FineTune-Suite

License

Apache 2.0

Base model

Qwen/Qwen2.5-7B

Qwen/Qwen2.5-7B-Instruct

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