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README.md

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+---
+license: mit
+base_model: microsoft/Phi-4-mini-instruct
+tags:
+- phi4
+- gguf
+- quantized
+- q4_k_m
+- buildsnpper
+- sap-assessor
+- chatbot
+- customer-support
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Buildsnpper SAP Assessor Platform Chatbot (Q4_K_M)
+
+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 Q4_K_M 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: qkv_proj, o_proj
+  - 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 Q4_K_M 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.
+
+## Contact
+
+- Organization: [bricksandbotltd](https://huggingface.co/bricksandbotltd)
+- Platform: [Buildsnpper SAP Assessor Platform](https://buildsnpper.com)