sentimentizer 0.330.0

smole language models

sentimentizer

Lightweight PyTorch models for sentiment analysis. Small models can be pretty effective for classification tasks at a much smaller cost to deploy — all models were trained on a single GPU in minutes, and inference requires less than 1GB of memory.

Beta release — API is subject to change.

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Install

# Install local-only version (no Ray dependency)
uv add sentimentizer

# Install with distributed training, tuning, and serving features
uv add "sentimentizer[ray]"

# Install with image generation (Stable Diffusion / FLUX)
uv add "sentimentizer[diffusion]"

# Install both
uv add "sentimentizer[ray,diffusion]"

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Quick Start

Run a pre-trained model locally:

from sentimentizer.predictor import SentimentPredictor

# Load the model
predictor = SentimentPredictor(model_name="encoder")

# Predict sentiment (returns label, score, token count, and model type)
result = predictor.predict("amazing restaurant!")
# >> {"label": "positive", "score": 0.92, "token_count": 2, "model": "encoder"}

# Batch prediction
results = predictor.predict_batch(["Great food!", "Terrible service."])
# >> [{"label": "positive", "score": 0.88, "token_count": 2, "model": "encoder"}, ...]

Models output 3-class probabilities (negative, neutral, positive) that sum to 1.0 per sample.

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Image Generation (SD 2.1 / FLUX.1-dev / SD 3.5 Medium)

The diffusion serving pipeline adds GPU-backed image generation endpoints alongside sentiment analysis. Disabled by default; enable via config.

Prerequisites

# Install with diffusion support (includes diffusers, transformers, accelerate, safetensors)
uv sync --extra diffusion

# For GPU: install CUDA-enabled PyTorch
uv sync --no-sources-package torch

Configure

Set environment variables or edit sentimentizer/serve/serve_config.yaml:

# Enable one or more models
sd_enabled: true
flux_enabled: false            # FLUX needs ~22 GB VRAM (L4/A100)
sd35_enabled: false            # SD 3.5 Medium needs ~5-6 GB VRAM
default_image_model: "sd"      # used when request omits model field

# Auth — required for image routes (/v1/images/*)
api_keys: ["sk-your-secret-key"]

# Optional: custom model paths
sd_model_id: "stabilityai/stable-diffusion-2-1"
sd35_model_id: "stabilityai/stable-diffusion-3.5-medium"
flux_model_path: "/path/to/flux1-dev-q8_0.gguf"   # GGUF quantized weights

Or via environment variables:

# Enable SD 2.1
export SENTIMENTIZER_SD_ENABLED=true
export SENTIMENTIZER_API_KEYS=sk-your-secret-key

# Or enable SD 3.5 Medium
export SENTIMENTIZER_SD35_ENABLED=true
export SENTIMENTIZER_API_KEYS=sk-your-secret-key

Run

# Start the Ray Serve deployment (loads model on startup)
python -m sentimentizer.serve

# SD 2.1 generation (sync, ~2-3s on L4)
curl -X POST http://localhost:8000/v1/images/generate \
  -H "Authorization: Bearer sk-your-secret-key" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a red apple on a wooden table", "model": "sd", "width": 512, "height": 512}'

# SD 3.5 Medium generation (sync, ~4-6s on L4)
curl -X POST http://localhost:8000/v1/images/generate \
  -H "Authorization: Bearer sk-your-secret-key" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a cinematic portrait of an astronaut", "model": "sd35", "width": 1024, "height": 1024}'

# List available models
curl http://localhost:8000/v1/images/models \
  -H "Authorization: Bearer sk-your-secret-key"

# Async job mode (for FLUX or long-running requests)
curl -X POST http://localhost:8000/v1/images/jobs \
  -H "Authorization: Bearer sk-your-secret-key" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a cinematic portrait of an astronaut", "model": "flux"}'

# Poll job status
curl http://localhost:8000/v1/images/jobs/{job_id} \
  -H "Authorization: Bearer sk-your-secret-key"

MPS (Apple Silicon) Support

SD 2.1 and SD 3.5 Medium work on MPS devices. FLUX GGUF is CUDA-only; setting flux_enabled: true on MPS raises RuntimeError at startup.

API Endpoints

Method	Path	Auth	Description
POST	/v1/images	Required	Synchronous image generation
POST	/v1/images/jobs	Required	Async job creation (201 + Location)
GET	/v1/images/jobs	Required	List jobs (paginated, scoped to API key)
GET	/v1/images/jobs/{id}	Required	Get job status
DELETE	/v1/images/jobs/{id}	Required	Cancel job (best-effort)
GET	/v1/images/models	Required	List available image models
GET	/v1/images/models/{name}	Required	Single model metadata

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Models

Four architectures are available:

Model	Module	Description
ModernBERT ⭐	sentimentizer.models.modernbert	ModernBERT contextual transformer backbone with mean pooling and layer-wise unfreezing — best performance
Encoder	sentimentizer.models.encoder	Transformer encoder with CLS token + positional encoding (4 layers, d_model=256)
RNN	sentimentizer.models.rnn	Bidirectional 2-layer LSTM (hidden=256) with pre-trained GloVe embeddings — solid baseline
Decoder	sentimentizer.models.decoder	Encoder-Decoder Transformer with learnable query token + cross-attention (2 encoder + 4 decoder layers)

All models output 3-class logits (B, 3) mapped to: negative (0), neutral (1), positive (2).

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Documentation

Detailed guides and implementation details are available in the specialized documentation files:

• 🚀 Model Serving Guide: Ray Serve application deployment, FastAPI endpoints (sentiment/routing/image generation), and the Go CLI client.
• 🎨 Diffusion Serving Plan: Image generation API design (SD 2.1, FLUX.1-dev, SD 3.5 Medium), middleware (auth, rate limiting, idempotency), and GPU deployment.
• 🏋️ Model Training & Checkpointing Guide: Yelp datasets, single-node/distributed commands, training arguments, sleep prevention, and checkpoint resuming.
• ⚙️ Model Configuration Reference: Configuration dataclasses (RNNConfig, EncoderConfig, etc.), parameter defaults, and consistency checks.
• 🎛️ Hyperparameter Tuning Guide: Optuna searches, LangGraph iterative agent tuning (via Ollama GLM 5.1), and validation/retries.
• 🔗 Hugging Face Hub Integration: Pre-trained weights synchronization, explicit pull/push, and auto-generated model cards.
• 📈 Metrics and Monitoring Pipeline: Exporter details, Grafana dashboards, Prometheus scrape targets, NaN handling, and real-time intra-epoch batch metrics.
• 🧭 SetFit Review Router: Utterance classification categories (Dietary/Service/General), Ollama GLM 5.1 augmentation, training, and evaluation.
• 🛠️ Troubleshooting Guide: Solutions for common issues like majority-class collapses, vocabulary matches, or scheduling.

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Development

This project uses uv for dependency management.

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies (CPU-only PyTorch, no Ray)
uv sync

# Install with Ray distributed features
uv sync --extra ray

# Install dev and test suites
uv sync --extra dev --extra ray

# Install with diffusion (image generation) support
uv sync --extra diffusion

# Full development install
uv sync --extra dev --extra ray --extra diffusion

Local CUDA / GPU development

The locked packages resolve CPU-only PyTorch. To install CUDA-enabled PyTorch locally:

uv sync --no-sources-package torch

Note: This ignores CPU overrides in pyproject.toml and pulls PyTorch from PyPI with CUDA/NVIDIA libraries. Avoid committing changes to uv.lock.

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Testing

Ensure local CI tests pass prior to submitting changes:

# Run all tests
uv run pytest tests/ -v

# Run only Ray Train tests
uv run pytest tests/ -v -k "Ray"

# Run with coverage report
uv run pytest tests/ -v --cov=sentimentizer --cov-report=term-missing

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Project Structure

sentimentizer/
├── __init__.py          # Logging and timing utilities
├── compat.py            # Transformers/setfit compatibility shims
├── config.py            # Configuration dataclasses and constants
├── data_source.py       # Unified DataSource protocol (pandas/Ray)
├── device.py            # Device detection (cuda/mps/cpu)
├── env.py               # Environment setup (NVIDIA LD_LIBRARY_PATH)
├── extractor.py          # Ray Data extraction from zip/tar archives
├── exporter.py           # Standalone Prometheus metrics exporter
├── export_onnx.py        # ONNX export, quantization, validation
├── hf.py                # Hugging Face Hub push/pull + model card generation
├── hf_dataset.py        # Dataset wrapper and collation for HF transformers
├── hf_tokenizer.py      # Tokenizer wrapper for HF transformers
├── loader.py             # Data loading utilities
├── losses.py             # FocalCrossEntropyLoss for 3-class training
├── metrics.py            # 3-class classification metrics (per-class P/R/F1, balanced accuracy, MCC)
├── metrics_publisher.py   # Epoch metrics publishing (Prometheus + JSON) + intra-epoch batch snapshots
├── predictor.py           # SentimentPredictor (model loading, inference)
├── safety.py              # Shared prompt safety (NSFW blocklist, injection patterns)
├── serve/                 # Ray Serve deployment: FastAPI + @serve.ingress, /v1/ prefix
│   ├── app.py             # FastAPI route handlers and deployment class
│   ├── base.py            # ServiceMetrics (request/latency tracking), _DummyServe fallback
│   ├── config.py           # Serve deployment configuration (YAML/env var loading, incl. cors_origins)
│   ├── middleware.py       # Auth, rate limiting, idempotency, prompt safety for image routes
│   ├── models.py          # Pydantic request/response models for Swagger docs
│   ├── diffusion_models.py # Pydantic request/response models for image generation (+ Job models)
│   └── diffusion_app.py    # SD/FLUX/SD35 deployments + ImagesDispatcher routes + job endpoints
├── diffusion/              # Diffusion model loading + inference
│   ├── config.py           # DiffusionModelConfig, SD/FLUX/SD35 default configs
│   ├── job_store.py         # JobStoreLogic + Ray actor for async job metadata
│   └── predictor.py         # DiffusionPredictor ABC, SDPredictor, FluxPredictor, SD35Predictor
├── tokenizer.py           # Text tokenizer with pre-trained support
├── trainer.py             # Training logic
├── tuner.py               # Ray Tune + Optuna hyperparameter search
├── data/                  # Training data (Yelp, GloVe)
├── agent/                 # LLM-guided tuning agent
│   ├── __init__.py       # Package exports
│   ├── config.yaml       # Agent + tuner configuration (YAML)
│   ├── loader.py         # YAML → dataclass config loader
│   ├── models.py         # Pydantic models (AnalysisResult, TuningDecision, etc.)
│   ├── agents.py         # Pydantic AI agents (GLM 5.1 via Ollama)
│   ├── prompts.py        # System prompts for analysis & strategy agents
│   ├── state.py          # LangGraph AgentState TypedDict
│   ├── nodes.py          # LangGraph node functions (analyze, decide, tune, evaluate)
│   ├── graph.py          # LangGraph StateGraph + run_agent_tuning() entry point
│   └── diagnose_model.py # TuningRun workflow (tune → train → validate → retry pipeline)
├── router/                # SetFit router module
│   ├── __init__.py       # Package exports
│   ├── config.py         # SetFitConfig, RouteLabels, AugmentConfig
│   ├── seeds.py          # Golden example utterances per category
│   ├── augment.py        # GLM 5.1 augmentation via Ollama
│   ├── dataset.py        # JSONL dataset loader, train/test split
│   ├── train_router.py   # SetFit training with compat shims
│   └── evaluate.py       # Similarity heatmap, threshold calibration
└── models/
    ├── __init__.py
    ├── base.py            # BaseSentimentModel with predict() and predict_text()
    ├── hf_base.py         # Base class for Hugging Face transformer architectures
    ├── rnn.py            # Bidirectional LSTM (3-class output)
    ├── encoder.py         # Transformer encoder model (3-class output)
    ├── decoder.py         # Encoder-decoder transformer (3-class output)
    └── modernbert.py      # ModernBERT transformer classifier wrapper (3-class output)

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License

MIT