Instructions to use bklassen3/softbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bklassen3/softbot with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bklassen3/softbot", filename="softball.Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bklassen3/softbot with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bklassen3/softbot:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bklassen3/softbot:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bklassen3/softbot:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bklassen3/softbot: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 bklassen3/softbot:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bklassen3/softbot: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 bklassen3/softbot:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bklassen3/softbot:Q4_K_M
Use Docker
docker model run hf.co/bklassen3/softbot:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bklassen3/softbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bklassen3/softbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bklassen3/softbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bklassen3/softbot:Q4_K_M
- Ollama
How to use bklassen3/softbot with Ollama:
ollama run hf.co/bklassen3/softbot:Q4_K_M
- Unsloth Studio
How to use bklassen3/softbot 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 bklassen3/softbot 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 bklassen3/softbot to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bklassen3/softbot to start chatting
- Atomic Chat new
- Docker Model Runner
How to use bklassen3/softbot with Docker Model Runner:
docker model run hf.co/bklassen3/softbot:Q4_K_M
- Lemonade
How to use bklassen3/softbot with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bklassen3/softbot:Q4_K_M
Run and chat with the model
lemonade run user.softbot-Q4_K_M
List all available models
lemonade list
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bklassen3/softbot:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf bklassen3/softbot: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 bklassen3/softbot:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf bklassen3/softbot: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 bklassen3/softbot:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf bklassen3/softbot:Q4_K_MUse Docker
docker model run hf.co/bklassen3/softbot:Q4_K_MQuick Links
Softball-Q4 (GGUF)
Format: GGUF (compatible with llama.cpp, llamafile, many runtimes)
Size: ~4.9 GB (q4_K_M quant)
Base model: Meta-Llama-3-8B-Instruct
Finetuning: LoRA merged on 09-12-2025, domain: softball analytics Q&A & text-to-SQL.
Intended Use
- Natural language Q&A and SQL assistance for softball data.
- Educational and exploratory analysis assistance.
Not intended for: safety-critical decisions or authoritative rule enforcement.
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Hardware compatibility
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4-bit
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf bklassen3/softbot:Q4_K_M# Run inference directly in the terminal: llama-cli -hf bklassen3/softbot:Q4_K_M