ps1811/advisor-minicpm-finetuned-gguf

Advisor MiniCPM Fine-Tuned GGUF

This model is a fine-tuned MiniCPM model for Advisor, a local-first Google Ads analysis app for small businesses. The model is designed to turn campaign, keyword, and search-term metrics into concise, actionable marketing recommendations.

It is used in the Advisor app here:

https://huggingface.co/spaces/build-small-hackathon/Advisor

Project repository:

https://github.com/PoornimaShridhar/Advisor

Intended Use

The model is tuned for short Google Ads advisory outputs, especially:

• campaign performance summaries
• keyword inspection
• search term cleanup
• concise action bullets grounded in metrics
• small-business-friendly explanations

In the app, the LLM is used for explanation-heavy cards:

• Ads Analyst
• Keyword Inspector
• Search Term Cleaner

Budget-sensitive decisions are intentionally handled by deterministic rule-based logic in the app, not delegated fully to the model.

Fine-Tuning Summary

The model was fine-tuned from:

openbmb/MiniCPM5-1B

The training workflow used QLoRA with 4-bit loading, then merged the LoRA adapter into the base model before converting the merged model to GGUF for local inference.

High-level process:

1. Prepared instruction-style chat examples in JSONL format.
2. Mixed synthetic Google Ads examples with cleaned campaign-style examples.
3. Trained a LoRA adapter with transformers, peft, trl, and bitsandbytes.
4. Merged the LoRA adapter into the base model.
5. Converted the merged model to GGUF with llama.cpp.
6. Quantized the GGUF model to Q4_K_M.
7. Loaded the final model locally through llama-cpp-python in the Advisor app.

Training Data Format

Each training record followed a chat-style JSONL structure:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a Google Ads analyst. Reply with concise actionable markdown bullets only."
    },
    {
      "role": "user",
      "content": "Analyze this Google Ads campaign data..."
    },
    {
      "role": "assistant",
      "content": "- Pause weak search terms with spend and no conversions.\n\n- Scale efficient keywords with conversions below target CPA."
    }
  ]
}

The fine-tuning target was not general conversation. The goal was to teach the model to write short, grounded, metric-aware recommendations.

Training Configuration

The project training script uses:

Training method: QLoRA
Max sequence length: 2048
Epochs: 2
Learning rate: 2e-4
Batch size: 2
Gradient accumulation: 8
LoRA rank: 16
LoRA alpha: 32
LoRA dropout: 0.05
Optimizer: paged_adamw_8bit
Quantization during training: 4-bit NF4

LoRA target modules:

q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Runtime

The Advisor app downloads this GGUF model with hf_hub_download and runs it locally with llama-cpp-python.

Default app configuration:

LLAMA_HF_REPO=ps1811/advisor-minicpm-finetuned-gguf
LLAMA_HF_FILENAME=advisor-minicpm-q4_k_m.gguf
LLAMA_N_CTX=2048
LLAMA_GPU_LAYERS=-1
LLAMA_N_THREADS=4

Example Output Style

The expected output style is concise markdown bullets:

- Treat "preschool near me" as a winning keyword because it produced conversions at an efficient CPA.

- Reduce spend on broad, low-intent terms that generated clicks but no leads.

- Add irrelevant search terms as negatives to protect budget for higher-intent traffic.

Limitations

• The model is specialized for Google Ads-style campaign analysis and may not perform well as a general assistant.
• It should not be used as the only source of truth for financial decisions.
• Budget changes in the Advisor app are handled by rule-based logic because spend decisions need predictable behavior.
• Outputs should be reviewed by a human before applying recommendations to a live ad account.

Privacy

The public repository does not include private Google Ads credentials or private training exports. Users running the app must provide their own Google Ads API credentials.