metadata
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
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"])