Instructions to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TracNetwork/functiongemma-270m-it-intercomswap-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TracNetwork/functiongemma-270m-it-intercomswap-v3")
model = AutoModelForCausalLM.from_pretrained("TracNetwork/functiongemma-270m-it-intercomswap-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TracNetwork/functiongemma-270m-it-intercomswap-v3",
	filename="gguf/functiongemma-v3-f16.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 TracNetwork/functiongemma-270m-it-intercomswap-v3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TracNetwork/functiongemma-270m-it-intercomswap-v3:F16
# Run inference directly in the terminal:
llama-cli -hf TracNetwork/functiongemma-270m-it-intercomswap-v3:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TracNetwork/functiongemma-270m-it-intercomswap-v3:F16
# Run inference directly in the terminal:
llama-cli -hf TracNetwork/functiongemma-270m-it-intercomswap-v3: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 TracNetwork/functiongemma-270m-it-intercomswap-v3:F16
# Run inference directly in the terminal:
./llama-cli -hf TracNetwork/functiongemma-270m-it-intercomswap-v3: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 TracNetwork/functiongemma-270m-it-intercomswap-v3:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TracNetwork/functiongemma-270m-it-intercomswap-v3:F16

Use Docker

docker model run hf.co/TracNetwork/functiongemma-270m-it-intercomswap-v3:F16

LM Studio
Jan

vLLM

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TracNetwork/functiongemma-270m-it-intercomswap-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TracNetwork/functiongemma-270m-it-intercomswap-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TracNetwork/functiongemma-270m-it-intercomswap-v3:F16

SGLang

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 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 "TracNetwork/functiongemma-270m-it-intercomswap-v3" \
    --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": "TracNetwork/functiongemma-270m-it-intercomswap-v3",
		"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 "TracNetwork/functiongemma-270m-it-intercomswap-v3" \
        --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": "TracNetwork/functiongemma-270m-it-intercomswap-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with Ollama:
```
ollama run hf.co/TracNetwork/functiongemma-270m-it-intercomswap-v3:F16
```

Unsloth Studio new

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 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 TracNetwork/functiongemma-270m-it-intercomswap-v3 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 TracNetwork/functiongemma-270m-it-intercomswap-v3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TracNetwork/functiongemma-270m-it-intercomswap-v3 to start chatting

Pi new

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TracNetwork/functiongemma-270m-it-intercomswap-v3: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": "TracNetwork/functiongemma-270m-it-intercomswap-v3:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TracNetwork/functiongemma-270m-it-intercomswap-v3: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 TracNetwork/functiongemma-270m-it-intercomswap-v3:F16

Run Hermes

hermes

Docker Model Runner
How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with Docker Model Runner:
```
docker model run hf.co/TracNetwork/functiongemma-270m-it-intercomswap-v3:F16
```

Lemonade

How to use TracNetwork/functiongemma-270m-it-intercomswap-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TracNetwork/functiongemma-270m-it-intercomswap-v3:F16

Run and chat with the model

lemonade run user.functiongemma-270m-it-intercomswap-v3-F16

List all available models

lemonade list

functiongemma-270m-it-intercomswap-v3

IntercomSwap fine-tuned FunctionGemma model for deterministic tool-calling in BTC Lightning <-> USDT Solana swap workflows.

What Is IntercomSwap

Intercom Swap is a fork of upstream Intercom that keeps the Intercom stack intact and adds a non-custodial swap harness for BTC over Lightning <> USDT on Solana via a shared escrow program, with deterministic operator tooling, recovery, and unattended end-to-end tests.

GitHub: https://github.com/TracSystems/intercom-swap

Base model: google/functiongemma-270m-it

Model Purpose

Convert natural-language operator prompts into validated tool calls.
Enforce buy/sell direction mapping for swap intents.
Support repeat/autopost workflows used by IntercomSwap prompt routing.

Repository Layout

./:
- merged HF checkpoint (Transformers format)
./nvfp4:
- NVFP4-quantized checkpoint for TensorRT-LLM serving
./gguf:
- functiongemma-v3-f16.gguf
- functiongemma-v3-q8_0.gguf

Startup By Flavor

1) Base HF checkpoint (Transformers)

python -m vllm.entrypoints.openai.api_server \
  --model TracNetwork/functiongemma-270m-it-intercomswap-v3 \
  --host 0.0.0.0 \
  --port 8000 \
  --dtype auto \
  --max-model-len 8192

Lower memory mode example:

python -m vllm.entrypoints.openai.api_server \
  --model TracNetwork/functiongemma-270m-it-intercomswap-v3 \
  --host 0.0.0.0 \
  --port 8000 \
  --dtype auto \
  --max-model-len 4096 \
  --max-num-seqs 8

2) NVFP4 checkpoint (`./nvfp4`)

TensorRT-LLM example with explicit headroom (avoid consuming all VRAM):

trtllm-serve serve ./nvfp4 \
  --backend pytorch \
  --host 0.0.0.0 \
  --port 8012 \
  --max_batch_size 8 \
  --max_num_tokens 16384 \
  --kv_cache_free_gpu_memory_fraction 0.05

Memory tuning guidance:

Decrease --max_num_tokens first.
Then reduce --max_batch_size.
Keep --kv_cache_free_gpu_memory_fraction around 0.05 to preserve safety headroom.

3) GGUF checkpoint (`./gguf`)

Q8_0 (recommended default balance):

llama-server \
  -m ./gguf/functiongemma-v3-q8_0.gguf \
  --host 0.0.0.0 \
  --port 8014 \
  --ctx-size 8192 \
  --batch-size 256 \
  --ubatch-size 64 \
  --gpu-layers 12

F16 (higher quality, higher memory):

llama-server \
  -m ./gguf/functiongemma-v3-f16.gguf \
  --host 0.0.0.0 \
  --port 8014 \
  --ctx-size 8192 \
  --batch-size 256 \
  --ubatch-size 64 \
  --gpu-layers 12

Memory tuning guidance:

Lower --gpu-layers to reduce VRAM usage.
Lower --ctx-size to reduce RAM+VRAM KV-cache usage.
Use q8_0 for general deployment, f16 for quality-first offline tests.

Training Snapshot

Base family: FunctionGemma 270M instruction-tuned.
Fine-tune objective: IntercomSwap tool-call routing and argument shaping.
Corpus profile: operations + intent-routing + tool-calling examples.

Evaluation Snapshot

From held-out evaluation for this release line:

Train examples: 6263
Eval examples: 755
Train loss: 0.01348
Eval loss: 0.02012

Intended Use

Local or private deployments where tool execution is validated server-side.
Deterministic operator workflows for swap infra.

Out-of-Scope Use

Autonomous financial decision-making.
Direct execution of unvalidated user text as shell/actions.
Safety-critical usage without host-side policy/validation.

Safety Notes

Always validate tool name + argument schema server-side.
Treat network-side payloads as untrusted input.
Keep wallet secrets and API credentials outside model context.

Provenance

Derived from: google/functiongemma-270m-it
Integration target: IntercomSwap prompt-mode tool routing

Downloads last month: 28

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for TracNetwork/functiongemma-270m-it-intercomswap-v3

Base model

google/functiongemma-270m-it

Quantized

(49)

this model