black279/Qwen_LeetCoder

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Model Card **

A lightweight Qwen2.5-0.5B model fine-tuned using Unsloth + LoRA (PEFT) for efficient text-generation tasks. This model is optimized for low-VRAM systems, fast inference, and rapid experimentation.

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Model Details

Model Description

This model is a parameter-efficient fine-tuned version of the base model:

• Base model: unsloth/qwen2.5-0.5b-unsloth-bnb-4bit
• Fine-tuning method: LoRA (PEFT)
• Quantization: 4-bit (bnb-4bit)
• Pipeline: text-generation
• Library: PEFT, Transformers, TRL, Unsloth

It is intended as a compact research model for text generation, instruction following, and as a baseline for custom SFT/RLHF projects.

• Developer: @Sriramdayal
• Repository: https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1
• License: Same as Qwen2.5 base license (typically Apache 2.0 or base model license)
• Languages: English (primary), multilingual capability inherited from Qwen2.5
• Finetuned from: unsloth/qwen2.5-0.5b-unsloth-bnb-4bit

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Model Sources

• GitHub Repo (Training Code): https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1

• Base Model: unsloth/qwen2.5-0.5b-unsloth-bnb-4bit

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Uses

Direct Use

• Instruction-style text generation
• Chatbot prototyping
• Educational or research experiments
• Low-VRAM inference (4–6 GB GPU)
• Fine-tuning starter model for custom tasks

Downstream Use

• Domain-specific SFT
• Dataset distillation
• RLHF training
• Task-specific adapters (classifiers, generators, reasoning tasks)

Out-of-Scope / Avoid

• High-accuracy medical/legal decisions
• Safety-critical systems
• Long-context reasoning competitive with large LLMs
• Harmful or malicious use cases

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Bias, Risks & Limitations

This model inherits all biases from Qwen2.5 training data and may generate:

• Inaccurate or hallucinated information
• Social, demographic, or political biases
• Unsafe or harmful recommendations if misused

Recommendations

Users must implement:

• Output filtering
• Safety moderation
• Human verification for critical tasks

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How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel

base = "unsloth/qwen2.5-0.5b-unsloth-bnb-4bit"
adapter = "black279/Qwen_LeetCoder"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    device_map="auto",
)

model = PeftModel.from_pretrained(model, adapter)

inputs = tokenizer("Hello!", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Data

The model was trained using custom datasets prepared through:

• Instruction datasets
• Synthetic Q&A
• Formatting for chat templates

(Replace with your actual dataset if you want more accuracy.)

Training Procedure

• Framework: Unsloth + TRL + PEFT

• Training type: Supervised Fine-Tuning (SFT)

• Precision: bnb-4bit quantization during training

• LoRA Ranks: (insert your actual values if different)

  • r=16, alpha=32, dropout=0.05

Hyperparameters

• Batch size: 2–8 (depending on VRAM)
• Gradient Accumulation: 8–16
• LR: 2e-4
• Epochs: 1–3
• Optimizer: AdamW / paged optimizers (Unsloth)

Speeds & Compute

• Hardware: 1× RTX 4090 / A100 / local GPU
• Training Time: 1–3 hours (approx)
• Checkpoint Size: Tiny (LoRA weights only)

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Evaluation

(You can update this later after running eval benchmarks.)

• Model evaluated on small reasoning + text-generation samples
• Performs well for short instructions
• Limited long-context and deep reasoning

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Environmental Impact

• Hardware: 1 GPU (consumer or cloud)
• Carbon estimate: Low (small model + LoRA)

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Technical Specs

• Architecture: Qwen2.5 0.5B
• Objective: Causal LM
• Adapters: LoRA (PEFT)
• Quantization: bnb 4-bit

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Citation

@misc{Sriramdayal2025QwenLoRA,
  title={Qwen2.5-0.5B Unsloth LoRA Fine-Tune},
  author={Sriram Dayal},
  year={2025},
  howpublished={\url{https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1}},
}

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Model Card Author

@Sriramdayal

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Framework versions

• PEFT 0.18.0