Qwen3.5-2B Excel Assistant — GGUF Q4_K_M

A lightweight Qwen3.5-2B model fine-tuned on ~2,000 Excel instruction–response pairs and quantized to GGUF Q4_K_M — shrinking from 2.7 GB down to 1.27 GB (52.96% smaller) for fast, fully local inference via Ollama or llama.cpp. Drop-in alternative to the larger 4B variant when running on resource-constrained hardware.

Companion model: Qwen3-4B Excel FT (higher accuracy) Training dataset: Nikhil1581/excel_dataset

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Model Details

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What It Does

This model is specialized for Excel and spreadsheet tasks, including:

• Writing and explaining Excel formulas (VLOOKUP, INDEX/MATCH, SUMIFS, array formulas)
• Data analysis guidance (pivot tables, conditional formatting, data validation)
• VBA macro writing and debugging
• Financial modelling and worksheet structuring
• Step-by-step spreadsheet troubleshooting

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Quick Start

Ollama (recommended)

# Pull and run directly
ollama run hf.co/Nikhil1581/qwen3.5-2b.Q4_K_M-excel_fine_tuning

llama.cpp

# Download the model file
wget https://huggingface.co/Nikhil1581/qwen3.5-2b.Q4_K_M-excel_fine_tuning/resolve/main/qwen3.5-2b-excel.Q4_K_M.gguf

# Run inference
./llama-cli -m qwen3.5-2b-excel.Q4_K_M.gguf \
  --chat-template qwen \
  -p "How do I use VLOOKUP to find a value across two sheets?" \
  -n 512

Python — llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="qwen3.5-2b-excel.Q4_K_M.gguf",
    n_ctx=2048,
    chat_format="qwen",
)

response = llm.create_chat_completion(
    messages=[
        {
            "role": "system",
            "content": "You are an expert Excel assistant. Provide clear, accurate spreadsheet help."
        },
        {
            "role": "user",
            "content": "Write a formula to calculate the running total of sales in column B, starting from B2."
        }
    ]
)

print(response["choices"][0]["message"]["content"])

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Example Prompts & Responses

Formula writing

User: How do I sum only the cells in column C where column A equals "Sales" and column B is greater than 100?

Model: Use SUMIFS:
=SUMIFS(C:C, A:A, "Sales", B:B, ">"&100)

This sums all values in column C where column A contains exactly "Sales" AND
column B is greater than 100. The "&" concatenates the operator with the number.

VBA

User: Write a VBA macro to highlight all cells in a selection that contain negative values in red.

Model: Sub HighlightNegatives()
    Dim cell As Range
    For Each cell In Selection
        If IsNumeric(cell.Value) And cell.Value < 0 Then
            cell.Interior.Color = RGB(255, 0, 0)
        End If
    Next cell
End Sub

Troubleshooting

User: My VLOOKUP returns #N/A even though the value exists. What's wrong?

Model: The most common causes are:
1. Leading/trailing spaces — use TRIM(lookup_value) to clean the search term
2. Number stored as text — format the column consistently
3. Exact match vs. approximate match — ensure the 4th argument is FALSE (or 0) for exact match
4. Lookup column isn't the leftmost column in your table_array

Try: =VLOOKUP(TRIM(A2), Sheet2!$A:$C, 2, FALSE)

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Evaluation Results

Evaluated on a held-out set of 751 Excel instruction prompts spanning formulas, VBA, financial modelling, and spreadsheet concepts. Pass/fail graded by keyword matching against expected outputs.

Fine-tuning delivers a 3.7× accuracy gain over the base model on domain-specific Excel tasks.

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Training Details

Training Loss Curve

Loss
1.20 │██
     │  ██
1.00 │    █
     │     ██
0.80 │       ██
     │         ███
0.60 │            ███
     │               ███
0.40 │                  ██████
     │                        ███
0.20 │                           ██
     └────────────────────────────── Steps
      25  50  75 100 125 150 175 200

Step-by-Step Loss

The sharpest drops occur at step 50→100 (−35.5%) and step 150→175 (−41.8%), indicating the model learned core formula syntax early and then refined nuanced task understanding in the final quarter of training. Loss plateaus briefly between steps 100–150 before a strong final descent, consistent with LoRA adapters settling into domain-specific knowledge.

For higher accuracy on complex tasks (array formulas, advanced VBA, financial modelling), use the 4B variant linked above.

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Hardware Requirements

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Limitations

• Optimized for English-language Excel tasks; non-English function names (e.g., German SVERWEIS) may not perform as well
• Complex multi-sheet workbook reasoning may require the larger 4B model
• Not intended for general-purpose chat; best results come from Excel-specific prompts
• Model may occasionally produce plausible-looking but incorrect formulas — always verify in your spreadsheet

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Related Resources

• Qwen3-4B Excel FT (larger, higher accuracy)
• excel_dataset — training data
• ARIA — AI Desktop Assistant (GitHub)

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Citation

If you use this model in research or a project, a mention is appreciated:

Nikhil Bisht (2026). Qwen3.5-2B Excel Fine-Tune (GGUF Q4_K_M).
HuggingFace. https://huggingface.co/Nikhil1581/qwen3.5-2b.Q4_K_M-excel_fine_tuning

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Built by @Nikhil1581 · MIT License