Text Generation
PEFT
Safetensors
English
qlora
data-science
code-generation
qwen2
lora
sft
unsloth
conversational
Instructions to use jsmall12/DataSci-Coder-14B-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jsmall12/DataSci-Coder-14B-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "jsmall12/DataSci-Coder-14B-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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README.md
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@@ -33,7 +33,7 @@ A QLoRA fine-tuned adapter for [Qwen2.5-Coder-14B-Instruct](https://huggingface.
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- Generates complete, runnable Python code for data science tasks
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- Covers statistics, machine learning, deep learning, NLP, time series, and visualization
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- Follows instructions precisely
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- Handles complex tasks: Bayesian inference, VAEs, GANs, survival analysis, stacking ensembles, SHAP, anomaly detection
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## Training Details
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- Generates complete, runnable Python code for data science tasks
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- Covers statistics, machine learning, deep learning, NLP, time series, and visualization
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- Follows instructions very precisely. For example, when told "no explanations," it outputs only code (base model ignores this 40% of the time)
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- Handles complex tasks: Bayesian inference, VAEs, GANs, survival analysis, stacking ensembles, SHAP, anomaly detection
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## Training Details
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