---
base_model:
- migtissera/Tess-4-27B
license: apache-2.0
library_name: transformers
tags:
- tess
- eagle3
- speculative-decoding
- draft-model
- qwen3
---

# Tess-4-27B · EAGLE-3 Draft

An **EAGLE-3 speculative-decoding draft head** for **[migtissera/Tess-4-27B](https://huggingface.co/migtissera/Tess-4-27B)** — trained on-policy against Tess-4's own outputs, so it predicts *this* model, not its base.

> **Measured: 1.76× faster decoding on average — up to 2.4× on reasoning.** Lossless: the target model verifies every token, so outputs are identical to non-speculative decoding.

## Benchmarks

Single H100-80GB, greedy decoding, 10 held-out prompts × 1024 tokens each:

| Workload | Baseline | With EAGLE-3 | Speedup |
|---|---|---|---|
| Math / logic reasoning | 51.2 tok/s | 103–123 tok/s | **2.0–2.4×** |
| Coding | 51.2 tok/s | 81–91 tok/s | 1.6–1.8× |
| Agentic planning | 51.2 tok/s | 83 tok/s | 1.6× |
| General chat | 51.2 tok/s | 80–87 tok/s | 1.6–1.7× |
| **Overall** | **51.2 tok/s** | **90.2 tok/s** | **1.76×** |

Server-reported accept length: **1.5–2.2 tokens per verification step** (conservative settings: `topk=1`, 4 draft tokens — tuning headroom remains).

## Why this exists

Tess-4-27B inherits an MTP head from its base model, but fine-tuning shifts the output distribution, driving that head's acceptance to ~0% — it can't accelerate the fine-tune. This EAGLE-3 draft was trained **against Tess-4 itself**: ~4,700 on-policy samples (Tess-4's own reasoning + coding + agentic generations, including real multi-turn tool-call traces), 2 epochs, with the draft consuming fused hidden states from three depths of the frozen target.

## Usage

### SGLang (tested ✅)

```bash
python -m sglang.launch_server \
  --model-path migtissera/Tess-4-27B \
  --speculative-algorithm EAGLE3 \
  --speculative-draft-model-path migtissera/Tess-4-27B-EAGLE3 \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --trust-remote-code
```

### vLLM

```bash
vllm serve migtissera/Tess-4-27B \
  --speculative-config '{"method": "eagle3", "model": "migtissera/Tess-4-27B-EAGLE3", "num_speculative_tokens": 4}' \
  --trust-remote-code
```

*(Benchmarked with SGLang; vLLM flags may vary by version.)*

## Details

- **Architecture**: 1-layer Llama-style EAGLE-3 head (hidden 5120, matching the target), draft vocab 32K mapped onto the target's 248K vocab. **1.22 GB.** Input embeddings are not included — the serving engine shares them from the target model.
- **Training**: [SpecForge](https://github.com/sgl-project/SpecForge) (SGLang team), online mode, frozen target, single H100.
- **Data**: on-policy — the target generated every training token. Mix: agentic/coding prompts from the Tess-4 program, OpenCoder, GSM8K, UltraChat, plus real multi-turn tool-call traces.
- **Lossless**: speculative decoding never changes outputs; the target verifies every proposed token. Speedup depends on workload (structured/reasoning content accepts more).

## License

Apache 2.0, matching [Tess-4-27B](https://huggingface.co/migtissera/Tess-4-27B).

*Part of the **Tess** series by [Migel Tissera](https://huggingface.co/migtissera).*