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Secure AI virtual data roomsOrnith-1.0-397B
Ornith-1.0-397B is a 397-billion-parameter open-source language model optimized for agentic coding tasks. Built on Qwen 3.5 and Gemma 4 foundations, it uses a mixture-of-experts architecture and reinforcement learning to improve code generation and agent reasoning. MIT licensed, free to use globally, with reported strong performance on coding benchmarks (Terminal-Bench, SWE-bench, NL2Repo). Designed for efficient single-GPU deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | deepreinforce-ai |
| Parameters | 396.8B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 94.1k |
| Likes | 224 |
| Last updated | 2026-06-25 |
| Source | deepreinforce-ai/Ornith-1.0-397B |
What Ornith-1.0-397B is
Ornith-1.0-397B is a 397B-parameter mixture-of-experts (MoE) model post-trained on Qwen 3.5 and Gemma 4 base models. It employs self-improving training via RL to jointly optimize solution scaffolds and rollouts. The model supports image-text-to-text and text-generation pipelines, formatted in safetensors, and is compatible with transformers-based inference. Context length is unknown; deployment targets single-GPU inference.
Run Ornith-1.0-397B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="deepreinforce-ai/Ornith-1.0-397B")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: 397B MoE parameters at ~2 bytes per active parameter (bfloat16 with sparse activation) ≈ ~200–250GB active memory during inference. Full model ~800GB. Requires multi-GPU (8× H100 / A100 or similar) or quantized variants (int8/int4) on single high-end GPU (≥80GB). Context length unknown; confirm before production planning.
No explicit fine-tuning guidance in card. MoE models add complexity to LoRA/QLoRA (router tuning, expert selection). Feasibility depends on whether fine-tuning infra supports sparse updates. Requires review of training scripts on HuggingFace repo. Recommend consulting deepreinforce-ai community or docs before committing to fine-tuning pipeline.
When to avoid it — and what to weigh
- Very Limited Hardware (< 80GB VRAM) — 397B parameters require substantial GPU memory, even with quantization. Not feasible on consumer-grade GPUs; requires enterprise-class hardware or cloud acceleration.
- Production Latency-Critical Applications — MoE models introduce routing overhead. If sub-100ms inference is mandatory, smaller dense models or optimized proprietary APIs may be preferable.
- Non-English or Domain-Specific Code Tasks — Training focused on agentic coding; coverage for non-English code, domain-specific languages (COBOL, Fortran, domain DSLs), or low-resource languages is unknown. Benchmark data is sparse.
- Fully Offline / Air-Gapped Deployment Without Validation — While open-source and gated=false, deployment at scale in secure environments requires security audit and clearance. Not addressed in card.
License & commercial use
MIT License. Permissive open-source license allowing commercial use, modification, and distribution with attribution. No regional restrictions stated. Model weights are globally accessible (gated=false).
MIT is a permissive OSI-approved license that explicitly permits commercial use, including in proprietary products, without requiring disclosure or source-code sharing. No commercial restrictions in the license itself. However: (1) deployment at scale in production requires infrastructure and support planning; (2) model performance and liability remain your responsibility; (3) any base-model dependencies (Qwen 3.5, Gemma 4) should be audited for commercial constraints—card does not detail those licenses. Recommended: review Qwen and Gemma licensing separately before shipping.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
No security audit, adversarial robustness, or red-teaming results disclosed. As a 397B model fine-tuned on code, it may inherit or amplify code-injection, SQL-injection, or system-prompt vulnerabilities from training data. In agentic coding scenarios (e.g., terminal execution), constrain input validation, sandbox execution, and output filtering strictly. No mention of data provenance, filtering for sensitive patterns, or training-data audits. Production deployment in security-sensitive contexts requires third-party review.
Alternatives to consider
Qwen 3.5-397B (base model)
Same parameter count, likely similar inference cost. Ornith is a post-trained variant; Qwen 3.5 base may be preferable if you want a general-purpose model and plan your own task-specific fine-tuning. No MIT license stated for Qwen 3.5; requires checking.
DeepSeek V4 Pro 1.6T
Larger, closed-source API-based alternative with strong SWE-bench results. Trade: no local control, potential vendor lock-in, but likely lower deployment complexity and commercial support.
Claude Opus 4.8 (via API)
Top SWE-bench Verified (87.6) and Pro (69.2) scores. Proprietary, managed service with commercial support. Higher cost per token, but eliminates infrastructure burden and security audit requirements for coding agent use cases.
Ship Ornith-1.0-397B with senior software developers
Ornith is strong for agentic coding tasks but demands significant compute. Start with a PoC on cloud infrastructure (AWS SageMaker, Anyscale, or Together AI). Audit base-model licenses and conduct a security review before production. Contact deepreinforce-ai for fine-tuning support and best practices.
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Ornith-1.0-397B FAQ
Can I use Ornith-1.0-397B in a commercial product?
What GPU hardware do I need to run this model?
How does Ornith compare to smaller, open-source models like CodeLlama or Granite?
Is fine-tuning supported?
Software developers & web developers for hire
Adopting Ornith-1.0-397B is usually one piece of a larger build. DEV.co provides custom software development services and web development — senior engineers working alongside your team to design, build, and operate open-source llms software in production.
Ready to Deploy Ornith-1.0-397B?
Ornith is strong for agentic coding tasks but demands significant compute. Start with a PoC on cloud infrastructure (AWS SageMaker, Anyscale, or Together AI). Audit base-model licenses and conduct a security review before production. Contact deepreinforce-ai for fine-tuning support and best practices.