Engrym Seed Base 9B
Orvyth's seed-tier brain — the recommended member of a local model family built for tool-using agents. Qwen3.5 hybrid linear-attention architecture, 262,144-token native context, first-class tool calling.
Weights are distributed via the Ollama registry.
ollama run Orvyth/engrym-seed:base
The ladder
| Tag | Class | Size | 77-task | Tool calls |
|---|---|---|---|---|
:nano |
Nano 2B | 2.1 GB | 90.8/143 | 12/12 |
:flash |
Flash 4B | 4.6 GB | 124/143 | 12/12 |
:base |
Base 9B | 9.5 GB | 131/143 | 12/12 |
:pro-27b-q4 |
Pro 27B v2 Q4 | 16.5 GB | 134/143 | 12/12 |
:pro |
Pro 27B v2 Q8 | 28.6 GB | 137/143 | 12/12 |
:pro-e |
Pro-E 27B (experimental) | 28.6 GB | 137/143 | 12/12 |
Evaluation
77 tasks · 143 points · temperature=0 · max_tokens=16384 · seed=42 · one attempt · deterministic
validators · no LLM judge. Scores are bound to the exact published blobs.
These are first-party numbers. Repeated runs on an uncontended GPU are deterministic (zero spread across n=2 for every model measured), but public reproduction receipts are still pending.
Compute modes — the score above is a floor
Asking the model to work deliberately (reason step by step, verify against every constraint, then answer) recovers points on tasks it otherwise fails. Base: 131 → 134. The gain is largest for the smallest models — Nano gains +11.8. On the small end, that is worth more than a model upgrade.
Defaults
temperature 0.2 · top_p 0.9 · top_k 20 · num_ctx 32768 · num_predict 8192
Native context is 262,144; larger requests are clamped. Default is 32,768 because defaulting to the
native maximum made a 9.5 GB model request ~19 GB of RAM to start. If a prompt exceeds num_ctx,
Ollama returns HTTP 400 — it does not silently truncate.
Lineage
| Stage | Provenance |
|---|---|
| Base | Qwen/Qwen3.5 — hybrid linear-attention |
| Merge | Ornith-1.0-9B × Qwythos-9B — TIES, 0.5 / 0.5 |
| Tune | Orvyth identity + chip-calling; LoRA merged into the weights |
| Build | Converted and quantized in-house with Orvyth trainkit |
What is in the artifact
Weights, identity, and tool-call generation. Memory, governed tool execution, safety enforcement, adapters and multi-agent routing are Orvyth platform concerns, not part of the GGUF. Tool calling is an output capability — the host validates, authorizes and executes.
Limits
- Scores are first-party and single-suite. Treat small gaps between adjacent models as unresolved.
- The identity tune is light; under a heavy external system prompt behavior can defer to the base model.
- The 27B is substantially slower per tool call than the 9B. Prefer Base or Flash for agent loops.
- The MTP speculative-decoding head is not included in these builds.
- Tags are mutable — pin the digest for production and evaluations.
ORVYTH — Intelligence. Governed. Ground truth over hype. Prove before you claim.