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agentionai/Signal-3.8-27B
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We evaluated Signal against the untouched Qwen3.8-27B, both at Q8_0 in llama.cpp, using the same server, sampling settings, prompts, and otherwise identical model file. All prompts in these evaluations were held out from tuning.
| base Q8_0 | Signal | change | |
|---|---|---|---|
| general answers, median tokens | 243 | 104 | -57% |
| answers opening with a preamble ("Sure!", "Great question") | 13% | 0% | gone |
| answers with markdown headers | 47% | 18% | -62% |
| answers with bold | 85% | 52% | -39% |
| coding answers, median tokens | 159 | 142 | -11% |
| coding answers, p90 tokens | 1026 | 914 | -11% |
Thinking mode, same prompts with reasoning on:
| base Q8_0 | Signal | change | |
|---|---|---|---|
| reasoning tokens, general prompts, median | 153 | 74 | -52% |
| reasoning tokens, coding prompts, median | 225 | 166 | -26% |
| reasoning tokens, GSM8K, median | 119 | 81 | -32% |
Quality, exact match on GSM8K:
| base Q8_0 | Signal | |
|---|---|---|
| thinking off, 60 problems | 98.3% | 98.3% |
| thinking on, 40 problems | 92.5% | 95.0% |
Shorter is not cheaper: no answer in the 100-prompt style set was cut off early (0 answers ending on a header or a colon, 0 unclosed code blocks), and no reasoning trace in 50 thinking-mode outputs looped or hit the token cap.
Qwen3.8-27B carries a built-in multi-token-prediction draft head. Signal's answers are more predictable, so the drafter agrees with the model more often:
Draft acceptance and decode speed with --spec-type draft-mtp, both models Q8_0 on the same
machine (Strix Halo, Vulkan), 200-token greedy runs for the fixed-draft rows:
| prompt / draft length | base acceptance | Signal acceptance | decode speed vs base |
|---|---|---|---|
| prose, draft 3 | 39% | 47% | +10% |
| prose, draft 4 | 35% | 28% | -9% |
| structured output (JSON), draft 3 | 72% | 94% | +20% |
| structured output (JSON), draft 4 | 66% | 87% |
The complete Qwen3.8-27B checkpoint, 18 safetensors shards in BF16, with one tensor replaced: lm_head.weight. Every other tensor, the vision encoder, the projector, the MTP draft head, the tokenizer and the chat template are byte-identical to Qwen/Qwen3.8-27B. The head delta has a norm of 4.1% of the original head. Vision input works as in the base model.
Because only the output layer differs, any quantization recipe, LoRA, or serving setup that works on Qwen3.8-27B works on Signal unchanged.
Thinking on and off both work; the chat template is the original Qwen3.8 template.
Sampling: temperature 0.7, top-p 0.95, top-k 20, min-p 0. Use sampling rather than greedy decoding. We saw a single loop at temperature 0.
python
from transformers import AutoProcessor, AutoModelForImageTextToTextmodel_id = "agentionai/Signal-3.8-27B"processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto")messages = [{"role": "user", "content": "Explain how a hash map works."}]inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7, top_p=0.95, top_k=20)print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
Thinking is on by default. Pass enable_thinking=False to apply_chat_template to turn it off.
bash
vllm serve agentionai/Signal-3.8-27B --dtype bfloat16 --max-model-len 65536 --reasoning-parser qwen3
Then call the OpenAI-compatible endpoint with the sampling settings above. Send
"chat_template_kwargs": {"enable_thinking": false} to turn thinking off per request.
Use the prebuilt tiers in agentionai/Signal-3.8-27B-GGUF, which include the vision projector and the multi-token-prediction draft head, or convert this checkpoint with convert_hf_to_gguf.py.
Signal3.8 is released freely. If it saves you compute or makes Qwen more useful, you can sponsor continued tuning, quantization and benchmarking on GitHub.
| +22% |
| chat prompts, sampled at 0.7, adaptive draft ≤4 (40 prompts) | 57% | 60% | — |