---
library_name: transformers
tags:
- text-generation-inference
- Code
- Math
- RL
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

![P.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/X7zeHYbH5Y5JoRK_ud_Ya.png)

# **Pictor-1338-QwenP-1.5B**

> **Pictor-1338-QwenP-1.5B** is a **code reasoning LLM** fine-tuned from **Qwen-1.5B** using **distributed reinforcement learning (RL)**. This model is designed to enhance coding proficiency, debugging accuracy, and step-by-step reasoning in software development tasks across multiple programming languages.

## **Key Features**

1. **Code Reasoning & Explanation**  
   Trained to **analyze, generate, and explain** code with a focus on logic, structure, and clarity. Supports functional, object-oriented, and procedural paradigms.

2. **Reinforcement Learning Fine-Tuning**  
   Enhanced using **distributed RL**, improving reward-aligned behavior in tasks like fixing bugs, completing functions, and understanding abstract instructions.

3. **Multi-Language Support**  
   Works fluently with **Python**, **JavaScript**, **C++**, and **Shell**, among others—ideal for general-purpose programming, scripting, and algorithmic tasks.

4. **Compact and Efficient**  
   At just **1.5B parameters**, it's lightweight enough for **edge deployments** and **developer tools** with strong reasoning capability.

5. **Debugging and Auto-Fix Capabilities**  
   Built to **identify bugs**, **recommend corrections**, and provide context-aware **explanations of issues** in codebases.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Pictor-1338-QwenP-1.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function that checks if a number is prime, and explain how it works."

messages = [
    {"role": "system", "content": "You are a code reasoning assistant. Your job is to write correct code and explain the logic step-by-step."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

## **Intended Use**

- **Code Assistance & IDE Integration**:  
  Smart autocomplete, bug detection, and function suggestion for developers.

- **Learning & Explanation**:  
  Ideal for **students** and **educators** in programming courses or interactive coding tutorials.

- **Automated Code Review & QA**:  
  Analyzes logic, structure, and potential bugs in code for quality assurance.

- **Edge & DevTool Deployments**:  
  Lightweight enough for browser extensions, local developer tools, and CLI-based assistants.

## **Limitations**

1. **Scaling Challenges**  
   May not handle large, complex codebases as well as larger models.

2. **Inconsistent Creativity**  
   May vary in performance for creative or unconventional coding tasks.

3. **Security Considerations**  
   Outputs should be audited to avoid insecure or vulnerable code patterns.

4. **Prompt Design Sensitivity**  
   Better output with clear instructions, function definitions, or examples.