---
base_model: deepseek-ai/deepseek-coder-6.7b-base
library_name: peft
pipeline_tag: text-generation
tags:
- deepseek-coder
- lora
- transformers
---

# Model Card: ColabMind-Coder-6.7B-LoRA

## Model Details
- **Base Model:** deepseek-ai/deepseek-coder-6.7b-base  
- **Technique:** LoRA fine-tuning with PEFT  
- **Language:** English, programming languages (Python, Machine Learning)  
- **Type:** Causal LM for code generation  

## Intended Uses
- **Direct Use:** Code completion, code explanation, small script generation  
- **Downstream Use:** Can be fine-tuned for domain-specific code tasks  
- **Out of Scope:** Malicious code generation, production-grade critical systems without human review  

## Training
- **Data:** Filtered samples from The-Stack-v2 & curated coding datasets  
- **Procedure:** LoRA fine-tuning on Google Colab (T4 GPU, 8GB VRAM)  
- **Precision:** Mixed fp16  

## Limitations
- May produce incorrect or insecure code  
- Bias from training data may persist  
- Not optimized for very large-scale projects  

## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model = AutoModelForCausalLM.from_pretrained("Agasthya0/colabmind-coder-6.7b-lora")
tokenizer = AutoTokenizer.from_pretrained("Agasthya0/colabmind-coder-6.7b-lora")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(pipe("def fibonacci(n):")[0]["generated_text"])