metadata
library_name: speculators
tags:
- eagle3
- speculative-decoding
- vllm
- qwen3.5
- moe
license: mit
language:
- en
- ru
EAGLE3 Speculator for Qwen3.5 MoE (a1-agents)
Trained using speculators library for speculative decoding with vLLM.
Model Details
- Verifier: Qwen3.5 MoE (Qwen3_5MoeForConditionalGeneration), 40 layers, 65.5 GB
- Draft type: EAGLE-3, 1 layer, draft vocab size 8192
- Target layers: [3, 19, 39]
- TTT steps: 3 (predicts 3 speculative tokens)
- Training data: 5000 ShareGPT samples
- Optimizer: Muon (muon_lr=1e-3, momentum=0.95)
- Attention: SDPA (required for GB10/Sm121)
Validation Metrics
| Position | Accuracy | Loss |
|---|---|---|
| 0 | 71.4% | 0.62 |
| 1 | 42.7% | 1.40 |
| 2 | 25.7% | 2.02 |
- Mean acceptance length: 2.02
- Throughput speedup: 1.34x (on GB10, CUDAgraph enabled)
Usage with vLLM
from vllm import LLM, SamplingParams
llm = LLM(
model="/path/to/verifier-model",
speculative_config={
"method": "eagle3",
"model": "Ichigec/a1-agents-eagle3-speculator",
"num_speculative_tokens": 3,
},
dtype="bfloat16",
gpu_memory_utilization=0.65,
max_model_len=8192,
enforce_eager=False, # CUDAgraph works for serving
)
Or with vLLM CLI:
vllm serve /path/to/verifier-model \
--speculative_config '{"method": "eagle3", "model": "Ichigec/a1-agents-eagle3-speculator", "num_speculative_tokens": 3}' \
--dtype bfloat16 \
--gpu-memory-utilization 0.65 \
--max-model-len 8192
Training Details
Trained on NVIDIA DGX Spark (GB10, 128GB unified memory) using the speculators offline pipeline.