---
license: other
license_name: embedl-models-community-licence-1.0
license_link: https://github.com/embedl/embedl-models/blob/main/LICENSE
base_model:
- google/gemma-3-1b-it
tags:
- text-generation-inference
---


# gemma-3-1b-it-FlashHead-W4A16

![My model banner](assets/FlashHead.png)

**Optimized version of gemma-3-1b-it using Quantization and FlashHead, Embedl’s efficient replacement for the language model head, reducing size while preserving accuracy.**
Designed for **low-latency inference** on **NVIDIA RTX GPUs**, leveraging:

- FlashHead
- Quantization (W4A16)
- Custom vLLM generation via `embedl-models`

FlashHead matches the gemma-3-1b-it baseline within rounding error on common benchmarks (MMLU-Pro, HellaSwag, GSM8K, etc.) and, combined with quantization, delivers SOTA on-device latency.

---

## Model Details
| **Field** | **Value** |
|------------|------------|
| **Base Model** | gemma-3-1b-it |
| **Input / Output** | Text → Text |
| **Release Date** | 2025-12-08 |
| **Version** | 1.0 |
| **Optimizations** | FlashHead LM Head, Quantization (W4A16) |
| **Developers** | Embedl |
| **Licenses** | Upstream: Gemma Terms of Use. <br>Optimized components: Embedl Models Community Licence v1.0 *(no redistribution)* |
| **Intended Use** | Text generation, reasoning, assistant-style interaction, and general-purpose NLP on NVIDIA RTX GPUs |

---

## Optimizations

- **FlashHead LM Head** - lightweight replacement for the dense LM head, significantly improving throughput.
- **Quantization (W4A16)** - large reduction in memory footprint and latency.
- **Custom Runtime Integration** - compatible with **vLLM (0.10.2)** via the `embedl-models` package.

---

## Performance

### Token Generation Speed (RTX 3500 Ada, batch size = 1)

| **Precision** | **Tokens/sec** | **Speedup vs BF16** |
|----------------|----------------|----------------------|
| BF16 baseline | 148 | 1.0× |
| **FlashHead (Embedl)** | **178** | **1.20×** |
| W4A16 baseline | 243 | 1.64x× |
| **FlashHead W4A16 (Embedl)** | **336** | **2.27×** |

FlashHead improves end-to-end speed by **1.38×** over state-of-the-art, while maintaining full accuracy parity.

**Measurement setup:** vLLM 0.10.2, batch_size=1, prompt length=32, max_new_tokens=128, 10 warm-up runs, averaged over 100 runs.

---

## Accuracy (Parity with Baseline)

| **Method** | **MMLU-Pro** | **IFEval** | **BBH** | **TruthfulQA** | **GSM8K** |
|-------------|---------------|--------------|-------------|----------------|--------------|
| **Baseline** | 0.15 | 0.55 | 0.38 | 0.31 | 0.42 |
| **FlashHead** | 0.15 | 0.49 | 0.38 | 0.31 | 0.39 |

FlashHead closely matches baseline accuracy.

---

## Installation

```bash
pip install embedl-models
```

The `embedl-models` package is required, it provides the optimized FlashHead implementation and quantized model runtime.

---

## Usage Examples
**Note (vLLM context length):** `max_model_len=131072` may fail on GPUs without enough free VRAM for the KV cache. If you see a KV cache memory error, lower `max_model_len` (or increase `gpu_memory_utilization`).

### vLLM Inference

```python
from vllm import SamplingParams
from embedl.models.vllm import LLM

model_id = "embedl/gemma-3-1b-it-FlashHead-W4A16"

if __name__ == "__main__":
    sampling = SamplingParams(max_tokens=128, temperature=0.0)
    llm = LLM(model=model_id, trust_remote_code=True, max_model_len=131072)
    
    prompt = "Write a haiku about coffee."
    output = llm.generate([prompt], sampling)
    print(output[0].outputs[0].text)
```

---

### Interactive REPL Example

The `run_repl()` coroutine launches an **interactive, streaming chat interface** using the vLLM backend with FlashHead enabled.  
It maintains an in-memory chat history and supports simple commands such as `/exit` to quit and `/reset` to clear context.

```python
import asyncio
from embedl.models.vllm.demo import run_repl

model_id = "embedl/gemma-3-1b-it-FlashHead-W4A16"

if __name__ == "__main__":
    asyncio.run(
        run_repl(
            model=model_id,
            max_model_len=131072
        )
    )
```
---

---

## ⚠️ Important Warning: Hugging Face Transformers Support

> **FlashHead is currently not applied when using the Hugging Face `transformers` pipeline.**  
> Generation through `transformers` will fall back to the standard dense LM head, **disabling FlashHead acceleration**.  
> 
> For now, **we strongly recommend using the vLLM integration** (`embedl.models.vllm.LLM`) to ensure FlashHead is active and optimized for low-latency inference.
>
> Full support for the Hugging Face `transformers` pipeline with FlashHead integration will be released **in the coming days**.

---

## Limitations

- Limited to **vLLM 0.10.2** (pinned dependency)
- **Batch size = 1** (real-time generation)
- Currently optimized for **NVIDIA RTX GPUs**

---

## Roadmap

Planned improvements:

- Advanced mixed precision quantization
- Huggingface transformers generation
- vLLM CLI benchmarking for detailed latency evaluation
- `lm-eval-harness` integration for detailed accuracy evaluation
- Upstream support in **Transformers** and **vLLM**
- Compatibility with **GGUF**, **MLC**, **Llama.cpp**, **Ollama**, etc.
- Broader model coverage (larger models, VLMs, VLAs)

---

## License

- **Upstream:** Gemma Terms of Use.
- **Optimized Components:** Embedl Models Community Licence v1.0 *(no redistribution)*

---

## Contact

**Enterprise & Commercial Inquiries**
[sales@embedl.com](mailto:sales@embedl.com)

**Technical Issues & Early Access**
[https://github.com/embedl/embedl-models](https://github.com/embedl/embedl-models)

**More Information & Model Releases**
[https://embedl.com](https://embedl.com)

---

### Partner & Developer Opportunities

If you are evaluating on-device inference, building products on SLMs, or exploring custom model optimization, reach out for:

- Embedl SDK - AI optimization tools & profiling
- Embedl HUB - benchmarking platform
- Engineering support for on-prem/edge deployments
- Migration guidance (Llama / Qwen / Gemma)
- Early access & partner co-marketing opportunities

Contact: [sales@embedl.com](mailto:sales@embedl.com)