Instructions to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bricksandbotltd/buildsnpper-chatbot-Q4_K_M", filename="buildsnpper-chatbot-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Use Docker
docker model run hf.co/bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bricksandbotltd/buildsnpper-chatbot-Q4_K_M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bricksandbotltd/buildsnpper-chatbot-Q4_K_M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
- Ollama
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with Ollama:
ollama run hf.co/bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
- Unsloth Studio new
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bricksandbotltd/buildsnpper-chatbot-Q4_K_M to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bricksandbotltd/buildsnpper-chatbot-Q4_K_M to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bricksandbotltd/buildsnpper-chatbot-Q4_K_M to start chatting
- Pi new
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with Docker Model Runner:
docker model run hf.co/bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
- Lemonade
How to use bricksandbotltd/buildsnpper-chatbot-Q4_K_M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M
Run and chat with the model
lemonade run user.buildsnpper-chatbot-Q4_K_M-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_MUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_MBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_MUse Docker
docker model run hf.co/bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_MBuildsnpper 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
# 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)
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
- Platform: Buildsnpper SAP Assessor Platform
- Downloads last month
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4-bit
Model tree for bricksandbotltd/buildsnpper-chatbot-Q4_K_M
Base model
microsoft/Phi-4-mini-instruct
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M# Run inference directly in the terminal: llama-cli -hf bricksandbotltd/buildsnpper-chatbot-Q4_K_M:Q4_K_M