Text Generation
Transformers
Safetensors
GGUF
swarm_moe
causal-lm
mixture-of-experts
swarm
agentic-runtime
smolagents
trust-remote-code
bf16
qwen-tokenizer
ollama-compatible-bridge
gguf-preview
full-runtime
conversational
custom_code
Instructions to use ayjays132/PhillSwarm-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayjays132/PhillSwarm-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ayjays132/PhillSwarm-4b", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ayjays132/PhillSwarm-4b", trust_remote_code=True, dtype="auto") - llama-cpp-python
How to use ayjays132/PhillSwarm-4b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ayjays132/PhillSwarm-4b", filename="phillswarm-4b-ollama-f16.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 ayjays132/PhillSwarm-4b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ayjays132/PhillSwarm-4b:F16 # Run inference directly in the terminal: llama-cli -hf ayjays132/PhillSwarm-4b:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ayjays132/PhillSwarm-4b:F16 # Run inference directly in the terminal: llama-cli -hf ayjays132/PhillSwarm-4b:F16
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 ayjays132/PhillSwarm-4b:F16 # Run inference directly in the terminal: ./llama-cli -hf ayjays132/PhillSwarm-4b:F16
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 ayjays132/PhillSwarm-4b:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ayjays132/PhillSwarm-4b:F16
Use Docker
docker model run hf.co/ayjays132/PhillSwarm-4b:F16
- LM Studio
- Jan
- vLLM
How to use ayjays132/PhillSwarm-4b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ayjays132/PhillSwarm-4b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayjays132/PhillSwarm-4b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ayjays132/PhillSwarm-4b:F16
- SGLang
How to use ayjays132/PhillSwarm-4b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ayjays132/PhillSwarm-4b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayjays132/PhillSwarm-4b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ayjays132/PhillSwarm-4b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayjays132/PhillSwarm-4b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use ayjays132/PhillSwarm-4b with Ollama:
ollama run hf.co/ayjays132/PhillSwarm-4b:F16
- Unsloth Studio
How to use ayjays132/PhillSwarm-4b 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 ayjays132/PhillSwarm-4b 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 ayjays132/PhillSwarm-4b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ayjays132/PhillSwarm-4b to start chatting
- Pi
How to use ayjays132/PhillSwarm-4b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ayjays132/PhillSwarm-4b:F16
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": "ayjays132/PhillSwarm-4b:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ayjays132/PhillSwarm-4b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ayjays132/PhillSwarm-4b:F16
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 ayjays132/PhillSwarm-4b:F16
Run Hermes
hermes
- Docker Model Runner
How to use ayjays132/PhillSwarm-4b with Docker Model Runner:
docker model run hf.co/ayjays132/PhillSwarm-4b:F16
- Lemonade
How to use ayjays132/PhillSwarm-4b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ayjays132/PhillSwarm-4b:F16
Run and chat with the model
lemonade run user.PhillSwarm-4b-F16
List all available models
lemonade list
| FROM ./phillswarm-4b-ollama-f16.gguf | |
| PARAMETER num_ctx 4096 | |
| PARAMETER temperature 0.65 | |
| PARAMETER top_p 0.9 | |
| PARAMETER repeat_penalty 1.05 | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|endoftext|>" | |
| TEMPLATE """{{- if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{- end }} | |
| {{- if .Tools }}<|im_start|>system | |
| # Tools | |
| You have access to the following functions: | |
| <tools> | |
| {{- range .Tools }} | |
| {{ . }} | |
| {{- end }} | |
| </tools> | |
| If a function is needed, answer with only: | |
| <tool_call> | |
| <function=tool_name> | |
| <parameter=name> | |
| value | |
| </parameter> | |
| </function> | |
| </tool_call> | |
| <|im_end|> | |
| {{- end }} | |
| {{- range .Messages }} | |
| <|im_start|>{{ .Role }} | |
| {{ .Content }}<|im_end|> | |
| {{- end }} | |
| <|im_start|>assistant | |
| """ | |
| SYSTEM """You are PhillSwarm-4B, a local tool-aware swarm MoE assistant. Answer the user's exact request directly. Use tools only when they are available and useful; when using tools, emit the configured XML tool-call format exactly. For normal chat, be concise, grounded, and practical.""" | |