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bricksandbotltd/buildsnpper-chatbot-Q4_K_M

sourceHugging Facemitupdated 10mo agoView on Hugging Face
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Buildsnpper SAP Assessor Platform Chatbot (Q4KM)

Fine-tuned Phi-4-mini-instruct model for the Buildsnpper SAP Assessor Platform customer support chatbot.

Model Details

  • —Base Model: microsoft/Phi-4-mini-instruct (3.8B parameters)
  • —Fine-tuning: LoRA (rank=16, alpha=32)
  • —Format: GGUF Q4KM quantized
  • —Size: ~2.5GB
  • —Context Length: 131,072 tokens
  • —Training Data: 89 Q&A pairs covering Buildsnpper platform features, workflows, and common user questions

Use Cases

This model is specifically trained to answer questions about:

  • —Project and client management in Buildsnpper
  • —Subscription and credit system
  • —Platform features and navigation
  • —Common technical issues
  • —Account management
  • —Report generation and exports

Usage

With llama.cpp

bash
# Download the model
wget https://huggingface.co/bricksandbotltd/buildsnpper-chatbot-Q4_K_M/resolve/main/buildsnpper-chatbot-Q4_K_M.gguf

# Run with llama.cpp
./llama-cli -m buildsnpper-chatbot-Q4_K_M.gguf -p "How do I create a new project in Buildsnpper?" -n 256

With Python (llama-cpp-python)

python
from llama_cpp import Llama

llm = Llama(
    model_path="buildsnpper-chatbot-Q4_K_M.gguf",
    n_ctx=2048,
    n_threads=4
)

response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "How do I assign credits to a client?"}
    ],
    temperature=0.1,
    max_tokens=256
)

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

Training Details

  • —LoRA Configuration:
  • —Rank: 16
  • —Alpha: 32
  • —Target modules: qkvproj, oproj
  • —Dropout: 0.05
  • —Training Parameters:
  • —Epochs: 3
  • —Learning rate: 3e-4
  • —Max sequence length: 1024
  • —Gradient accumulation: 4 steps
  • —Final training loss: 1.42
  • —Hardware: Apple M3 MacBook Air (MPS acceleration)
  • —Training time: ~1.5 hours

Quantization

Original FP16 model (7.67GB) was quantized to Q4KM format (2.5GB) using llama.cpp, achieving:

  • —67% size reduction
  • —Optimized for CPU inference
  • —Minimal quality degradation

Limitations

  • —Specialized for Buildsnpper platform only
  • —May not perform well on general queries outside the platform domain
  • —Designed for customer support, not general conversation

License

MIT License - See base model license for additional restrictions.

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