Instructions to use Orvyth/engrym-seed-pro-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Orvyth/engrym-seed-pro-27b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Orvyth/engrym-seed-pro-27b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Orvyth/engrym-seed-pro-27b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Orvyth/engrym-seed-pro-27b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Orvyth/engrym-seed-pro-27b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Orvyth/engrym-seed-pro-27b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Orvyth/engrym-seed-pro-27b
- SGLang
How to use Orvyth/engrym-seed-pro-27b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Orvyth/engrym-seed-pro-27b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Orvyth/engrym-seed-pro-27b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Orvyth/engrym-seed-pro-27b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Orvyth/engrym-seed-pro-27b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Orvyth/engrym-seed-pro-27b with Docker Model Runner:
docker model run hf.co/Orvyth/engrym-seed-pro-27b
Engrym Seed Pro 27B v2
Part of Engrym Seed — Orvyth's seed-tier local model family for tool-using agents. Qwen3.5 hybrid linear-attention architecture, 262,144-token native context, first-class tool calling.
The strongest stable artifact. Substantially slower per tool call — prefer Base or Flash for agent loops.
Weights are distributed via the Ollama registry.
ollama run Orvyth/engrym-seed:pro
| Size | 28.6 GB |
| 77-task score | 137/143 (95.8%) |
| Tool calling | 12/12 verified |
| Context | 262,144 native (default num_ctx 32,768) |
| Quantization | Q8_0 |
The full ladder
| Tag | Class | Size | 77-task |
|---|---|---|---|
:nano |
Nano 2B | 2.1 GB | 90.8/143 |
:flash |
Flash 4B | 4.6 GB | 124/143 |
:base |
Base 9B | 9.5 GB | 131/143 |
:pro-27b-q4 |
Pro 27B v2 Q4 | 16.5 GB | 134/143 |
:pro |
Pro 27B v2 Q8 | 28.6 GB | 137/143 |
:pro-e |
Pro-E 27B (experimental) | 28.6 GB | 137/143 |
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.
First-party numbers. Repeated runs on an uncontended GPU are deterministic (zero spread across n=2 for every model measured). Public reproduction receipts are pending.
Compute modes — the score is a floor
Asking the model to work deliberately recovers points on tasks it otherwise fails, and the gain is largest for the smallest models (Nano +11.8, Flash +6, Base +3, Pro-E +0). 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
If a prompt exceeds num_ctx, Ollama returns HTTP 400 — it does not silently truncate.
Lineage
Qwen/Qwen3.5 base → Ornith-1.0-9B x Qwythos-9B TIES merge (9B line) → Orvyth identity and
chip-calling LoRA merged into the weights → converted and quantized in-house.
Limits
- First-party scores; treat small gaps between adjacent models as unresolved.
- The identity tune is light; a heavy external system prompt can pull behavior toward the base model.
- The 27B is much slower per tool call than the 9B and smaller.
- No MTP speculative-decoding head in these builds.
- Tags are mutable — pin the digest for production and evaluations.
ORVYTH - Intelligence. Governed. Ground truth over hype. Prove before you claim.