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--- |
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base_model: deepseek-ai/deepseek-coder-6.7b-base |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- deepseek-coder |
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- lora |
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- transformers |
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--- |
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# Model Card: ColabMind-Coder-6.7B-LoRA |
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## Model Details |
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- **Base Model:** deepseek-ai/deepseek-coder-6.7b-base |
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- **Technique:** LoRA fine-tuning with PEFT |
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- **Language:** English, programming languages (Python, Machine Learning) |
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- **Type:** Causal LM for code generation |
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## Intended Uses |
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- **Direct Use:** Code completion, code explanation, small script generation |
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- **Downstream Use:** Can be fine-tuned for domain-specific code tasks |
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- **Out of Scope:** Malicious code generation, production-grade critical systems without human review |
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## Training |
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- **Data:** Filtered samples from The-Stack-v2 & curated coding datasets |
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- **Procedure:** LoRA fine-tuning on Google Colab (T4 GPU, 8GB VRAM) |
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- **Precision:** Mixed fp16 |
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## Limitations |
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- May produce incorrect or insecure code |
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- Bias from training data may persist |
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- Not optimized for very large-scale projects |
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## Quick Start |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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model = AutoModelForCausalLM.from_pretrained("Agasthya0/colabmind-coder-6.7b-lora") |
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tokenizer = AutoTokenizer.from_pretrained("Agasthya0/colabmind-coder-6.7b-lora") |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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print(pipe("def fibonacci(n):")[0]["generated_text"]) |
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