Instructions to use connectedtechco/syrup-code-x0.1-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use connectedtechco/syrup-code-x0.1-4b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="connectedtechco/syrup-code-x0.1-4b",
	filename="gguf/syrup-code-x0.1-4b-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use connectedtechco/syrup-code-x0.1-4b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf connectedtechco/syrup-code-x0.1-4b:F16
# Run inference directly in the terminal:
llama cli -hf connectedtechco/syrup-code-x0.1-4b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf connectedtechco/syrup-code-x0.1-4b:F16
# Run inference directly in the terminal:
llama cli -hf connectedtechco/syrup-code-x0.1-4b: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 connectedtechco/syrup-code-x0.1-4b:F16
# Run inference directly in the terminal:
./llama-cli -hf connectedtechco/syrup-code-x0.1-4b: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 connectedtechco/syrup-code-x0.1-4b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf connectedtechco/syrup-code-x0.1-4b:F16

Use Docker

docker model run hf.co/connectedtechco/syrup-code-x0.1-4b:F16

LM Studio
Jan
Ollama
How to use connectedtechco/syrup-code-x0.1-4b with Ollama:
```
ollama run hf.co/connectedtechco/syrup-code-x0.1-4b:F16
```

Unsloth Studio

How to use connectedtechco/syrup-code-x0.1-4b 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 connectedtechco/syrup-code-x0.1-4b 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 connectedtechco/syrup-code-x0.1-4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for connectedtechco/syrup-code-x0.1-4b to start chatting

How to use connectedtechco/syrup-code-x0.1-4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf connectedtechco/syrup-code-x0.1-4b: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": "connectedtechco/syrup-code-x0.1-4b:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use connectedtechco/syrup-code-x0.1-4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf connectedtechco/syrup-code-x0.1-4b: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 connectedtechco/syrup-code-x0.1-4b:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use connectedtechco/syrup-code-x0.1-4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf connectedtechco/syrup-code-x0.1-4b:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "connectedtechco/syrup-code-x0.1-4b:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use connectedtechco/syrup-code-x0.1-4b with Docker Model Runner:
```
docker model run hf.co/connectedtechco/syrup-code-x0.1-4b:F16
```

Lemonade

How to use connectedtechco/syrup-code-x0.1-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull connectedtechco/syrup-code-x0.1-4b:F16

Run and chat with the model

lemonade run user.syrup-code-x0.1-4b-F16

List all available models

lemonade list

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Syrup Code x0.1 4b

Syrup Code x0.1 4b is an experimental coding model fine-tuned from Qwen3 4B Instruct with Connected Tech codebase structure, specialized for generating structured code in Go, TypeScript, React, and Svelte.

Quantized GGUF variants: syrup-code-x0.1-4b-f16, syrup-code-x0.1-4b-q4_k, syrup-code-x0.1-4b-q8_0

Usage

Command Line (llama.cpp)

hf download connectedtechco/syrup-code-x0.1-4b gguf/syrup-code-x0.1-4b-(VARIANT).gguf --local-dir .
./llama-server --host 0.0.0.0 --port 8080 -m gguf/syrup-code-x0.1-4b-(VARIANT).gguf

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("connectedtechco/syrup-code-x0.1-4b")
tokenizer = AutoTokenizer.from_pretrained("connectedtechco/syrup-code-x0.1-4b")

prompt = "write a svelte component for navigation menu"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data: Connected Tech codebase
Training Method: LoRA fine-tuning

Limitations

Experimental model, may produce incorrect output
Optimized for Connected Tech codebase structure, intended for internal use only.

Citation

@misc{syrup-code-x0.1-4b,
  author = {Bhumjate S.},
  title = {Syrup Code x0.1 4b},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/connectedtechco/syrup-code-x0.1-4b}
}

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Safetensors

Model size

4B params

Tensor type

F16

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