README.md

---
base_model: google/functiongemma-270m-it
library_name: transformers
pipeline_tag: text-generation
license: gemma
tags:
- intercomswap
- function-calling
- tool-calling
- lightning
- solana
- gemma
---

# 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](https://huggingface.co/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)

```bash
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:

```bash
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):

```bash
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):

```bash
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):

```bash
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