Nikhil1581/qwen3-4b-instruct-2507.Q4_K_M-excel-finetuning-1.2kdataset

Qwen3-4B-Instruct-2507 — Excel Fine-Tune (Q4KM)

A QLoRA fine-tuned version of Qwen/Qwen3-4B-Instruct-2507, specialized for Excel and spreadsheet tasks. Quantized to GGUF Q4KM for local deployment via Ollama or LM Studio.

Model Details

Evaluation Results

Manually evaluated on a 30-prompt benchmark spanning standard and advanced Excel tasks:

Evaluation was performed via manual testing. A response was marked correct if it produced a working, usable formula or macro without requiring correction.

Training Performance

• —Loss reduction: 78.7% (1.04 → 0.22) over 200 steps
• —Framework: Unsloth (2x faster training pipeline)
• —Trainable parameters: ~132K (QLoRA adapters only)

Intended Use

This model is fine-tuned to assist with Excel and spreadsheet workflows:

• —Writing and explaining Excel formulas (VLOOKUP, INDEX/MATCH, XLOOKUP, array formulas, etc.)
• —Debugging broken formulas
• —Data analysis with Excel (pivot tables, conditional formatting, data validation)
• —VBA macro generation and explanation
• —Converting between Excel functions and Python/Pandas equivalents
• —Step-by-step spreadsheet task guidance

Quick Start

Ollama

ollama pull Nikhil1581/qwen3-4b-instruct-2507.Q4_K_M-excel-finetuning-1.2kdataset
ollama run Nikhil1581/qwen3-4b-instruct-2507.Q4_K_M-excel-finetuning-1.2kdataset

LM Studio

1. 1.Search for Nikhil1581/qwen3-4b-instruct-2507 in the model browser
2. 2.Download the Q4_K_M variant
3. 3.Load and chat

llama.cpp

./llama-cli -m qwen3-4b-excel-q4_k_m.gguf \
  --chat-template chatml \
  -p "You are an Excel expert assistant." \
  -i

Example Prompts

Formula help:

User: How do I look up a value in column A and return the corresponding value from column C?

Debugging:

User: My VLOOKUP returns #N/A even though the value exists. Why?

VBA:

User: Write a VBA macro to loop through all sheets and highlight cells greater than 1000 in red.

Data analysis:

User: How do I calculate a running total in Excel without using a helper column?

Training Details

• —Fine-tune type: QLoRA (4-bit quantized LoRA)
• —LoRA rank: 16
• —LoRA alpha: 32
• —Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• —Dataset: ~1,200 Excel/spreadsheet instruction-response pairs
• —Sequence length: 1,024 tokens
• —Epochs: 3
• —Steps: 200
• —Optimizer: pagedadamw8bit
• —Hardware: T4 GPU (Google Colab)
• —Framework: HuggingFace TRL + PEFT + Unsloth

Limitations

• —Focused on Excel — general coding or math reasoning may be weaker than the base model
• —Dataset is English-only
• —Q4KM quantization may reduce precision on very complex multi-step formula chains
• —Not tested on Google Sheets or LibreOffice Calc (though most formulas transfer)
• —Evaluation was manual (25 standard + 5 advanced prompts) — not a formal benchmark

Recommended Inference Settings

temperature: 0.3
top_p: 0.9
repeat_penalty: 1.1
num_predict: 512

Low temperature (0.3) is recommended to keep formula syntax accurate.

Author

Nikhil1581 — HuggingFace Profile

Acknowledgements

• —Qwen Team for the base model
• —Unsloth for the 2x faster training framework
• —HuggingFace TRL for the fine-tuning pipeline
• —llama.cpp for GGUF quantization