gemma-4-e2b-flowcast

gemma-4-e2b-flowcast · flowcast-sota-v1

Flowcast is a production LoRA fine-tune of Gemma 4 E2B for macOS voice-agent desktop automation. It powers spoken-command planning, intent routing, and dictation polish in GemmaFlow.

Say it. Plan it. Do it.

What it does

Benchmarks (Phase 3 production gate)

Inference stack: hybrid_slim prompts · JSON early-stop · MLX prefix KV cache.

Quick start (MLX, Apple Silicon)

pip install mlx-lm huggingface_hub

from huggingface_hub import snapshot_download
from mlx_lm import load, generate

base = snapshot_download("mlx-community/Gemma4-E2B-IT-Text-int4")
adapter = snapshot_download("nsalerni/gemma-4-e2b-flowcast")

model, tokenizer = load(base, adapter_path=adapter)

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Classify: go to gmail"}],
    tokenize=False,
    add_generation_prompt=True,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=128))

GemmaFlow / flowcast integration

from huggingface_hub import snapshot_download
from gemmaflow_tune import create_production_runner
from gemmaflow_tune.prompts import build_automation_planning_for_case

# Point runner at the published adapter (override local manifest path)
adapter = snapshot_download("nsalerni/gemma-4-e2b-flowcast")
runner = create_production_runner(adapter_path=adapter)

case = {"transcript": "go to gmail", "tags": ["web_routing"]}
prompt = build_automation_planning_for_case(case, "hybrid_slim")
result = runner.generate("", user_content=prompt, max_tokens=256)
print(result.text)

Note: create_production_runner(adapter_path=...) requires gemmaflow-tune installed from the training repo. For standalone MLX usage, use mlx_lm.load as shown above.

Files

Recommended inference settings

{
  "prompt_mode": "hybrid_slim",
  "json_early_stop": true,
  "use_prompt_kv_cache": true,
  "temperature": 0.02,
  "top_p": 0.85,
  "max_tokens": 256
}

Training

Fine-tuned with gemmaflow-tune:

• Base: mlx-community/Gemma4-E2B-IT-Text-int4
• Method: LoRA (rank 16, 16 layers, attn projections)
• Pipeline: SFT surgical null-plan refinement → Phase 2/3 prompt engineering
• Not included: fast-path retrain (hybrid_slim_fast, ~89% — rejected)

Citation

@misc{gemma4e2bflowcast2026,
  title  = {gemma-4-e2b-flowcast: Voice Desktop Automation for GemmaFlow},
  author = {Salerni, Nicola},
  year   = {2026},
  url    = {https://huggingface.co/nsalerni/gemma-4-e2b-flowcast}
}

License

Apache 2.0. Base model subject to Google Gemma license.