---
library_name: transformers
tags:
- text-generation-inference
- code
- math
- sft
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

![5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oRjYbuQFy0ajchUguRXKO.png)

# **Lambda-Cancri-1.5B-Exp**

> **Lambda-Cancri-1.5B-Exp** is a **general-purpose LLM** fine-tuned from **Qwen2.5-1.5B** using **optimized supervised fine-tuning (SFT)**. This model is designed to enhance coding proficiency, reasoning ability, and step-by-step explanation across multiple software development and general knowledge tasks.

## **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. **Optimized Supervised Fine-Tuning (SFT)**  
   Fine-tuned using **high-quality supervised datasets**, ensuring strong performance in tasks like code generation, bug fixing, function completion, and abstract reasoning.

3. **Multi-Language & General Task Support**  
   Works fluently with **Python**, **JavaScript**, **C++**, **Shell**, and general knowledge tasks — ideal for programming, scripting, algorithmic problem-solving, and general-purpose reasoning.

4. **Compact and Efficient**  
   At just **1.5B parameters**, it's lightweight enough for **edge deployments**, **developer tools**, and **general-purpose assistants**, while maintaining strong reasoning and coding capabilities.

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

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Lambda-Cancri-1.5B-Exp"

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 helpful coding and 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, function suggestion, and explanations for developers.

- **Learning & Education**:  
  Perfect for **students**, **educators**, and **self-learners** in programming and technical fields.

- **Automated Code Review & QA**:  
  Assists in logic analysis, structure evaluation, and bug spotting in code for quality assurance.

- **General Purpose Assistant**:  
  Provides help beyond coding—answering general queries, solving reasoning tasks, and assisting in workflows.

- **Edge & DevTool Deployments**:  
  Lightweight for browser extensions, desktop applications, and CLI-based assistants.

## **Limitations**

1. **Scaling Challenges**  
   May not handle extremely large or highly complex projects as well as larger models.

2. **Creativity Variability**  
   May show inconsistent performance on highly creative or unconventional coding tasks.

3. **Security Considerations**  
   Outputs should be audited to ensure the generation of secure, safe code.

4. **Instruction Sensitivity**  
   Responds better with clear, structured prompts and task instructions.