Tercet-R-1.0
Reasoning + tool-call chat model (~502M) — Tercet-R family
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A ~502M hybrid GDN-2 + GQA model, supervised fine-tuned for thinking and tools
What this is
Tercet-R-1.0 is the first public reasoning / tool-use checkpoint in the Tercet-R line.
- Base (pretrain):
kerzgrr/Tercet-base - Sibling chat model (no think toggle):
kerzgrr/Tercet - Hub weights are the EMA snapshot in bfloat16
Chat contract
Thinking
Each assistant turn is prefixed with a zero-loss control token:
| Mode | Prefix | Typical body |
|---|---|---|
| think | <|think|>\n | <think>…</think> then the answer |
| no-think | <|no_think|>\n | answer only |
inference.py and the local scripts/chat.py stream the <think> region live (dim yellow) and hide the control tokens.
Tool calls (SmolTalk JSON)
<tool_call>
{"name": "web_search", "arguments": {"query": "…"}}
</tool_call>
Tool results
Each observation is a tool (or user) turn prefixed with the special token:
<|tool_response|>
{observation}
inference.py --tools web_search pauses after a <tool_call>, you paste the search result, and generation continues.
Install & run
pip install torch safetensors tokenizers huggingface_hub
hf download kerzgrr/Tercet-R-1.0 inference.py --local-dir .
python inference.py
python inference.py --prompt "What is the capital of France?"
python inference.py --tools web_search
python inference.py --no-think --prompt "Reply in one sentence."
inference.py auto-downloads weights / tokenizer / tiny_gdn/ and auto-installs pinned flash-linear-attention. Git is required on PATH.
| Flag | Default | Description |
|---|---|---|
--prompt | — | One-shot user message |
--system | — | System prompt, used verbatim |
--think / --no-think | think | Assistant control prefix |
--tools | — | Built-in tools (web_search) |
--temperature | 0.7 | Sampling temperature |
--max-new-tokens | 4096 | Max generation length |
--device | cuda if available | cuda / cpu |
Interactive commands: /think /no_think /system … /reset /exit.
Model architecture
Same TinyGDN hybrid as Tercet-base (501,635,264 parameters):
| Layers | 32 (GDN-2 ×3 + GQA every 4th) |
| Hidden | 1,024 |
| MLP | SwiGLU 2,624 |
| Attention | 8 Q / 2 KV, head dim 128, partial RoPE |
| Linear | Gated DeltaNet-2, 8 heads × 128 |
| Vocab | 49,152 BPE |
| Context | 16,384 |
Training
| Stage | Details |
|---|---|
| Base | HuggingFaceFW/fineweb-edu → Tercet-base |
| Mid-SFT | HuggingFaceTB/smoltalk2 Mid: Llama-Nemotron-Post-Training-Dataset + OpenThoughts3-1.2M |
| Instruct SFT | HuggingFaceTB/smoltalk2 SFT (SmolTalk, OpenHermes-2.5, OpenThoughts3, Aya, Hermes function calling, s1K, Tulu-3 personas IF, xLAM, LongAlign, Mixture-of-Thoughts, …), seq 16,384, AdamW 5×10⁻⁵, 61.1 hours |
| Checkpoint | optimizer step 4,500 (latest complete instruct snapshot) |
| Weights | EMA (this repo's model.safetensors) |
| Val loss (EMA) | 1.744 (ppl 5.72) |
Limitations
- Scale: ~502M is a research / edge model, not a frontier system
- Requires
flash-linear-attention; not GGUF / llama.cpp compatible today
Model family
| Model | Stage | Hub |
|---|---|---|
| Tercet-base | Pretrain | kerzgrr/Tercet-base |
| Tercet | SFT chat | kerzgrr/Tercet |
| Tercet-R-1.0 | SFT reasoning + tools | this repo |
| Tercet-R-1.1 | Stage-3 SFT (latest) | kerzgrr/Tercet-R-1.1 |
Citation
@misc{tercetr2026,
title={Tercet-R-1.0: A 502M Hybrid GDN-2 + GQA Reasoning Model},
author={kerzgrr},
year={2026},
url={https://huggingface.co/kerzgrr/Tercet-R-1.0}
}
R is for reasoning.