Ornith-1.0-35B
Ornith-1.0-35B is a 35B-parameter open-source LLM optimized for agentic coding tasks. It uses a mixture-of-experts (MoE) architecture built on Qwen 3.5 and employs reinforcement learning to improve code generation. MIT-licensed, it's designed for single-GPU deployment and shows strong benchmark performance on code-related tasks like SWE-Bench and Terminal-Bench.
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 | 664944 |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 280.2k |
| Likes | 357 |
| Last updated | 2026-06-25 |
| Source | deepreinforce-ai/Ornith-1.0-35B |
What Ornith-1.0-35B is
Ornith-1.0-35B is a MoE-based language model with 664,944 total parameters (exact active parameter count unknown). Architecture is post-trained on Qwen 3.5 MoE base. Training uses a self-improving RL framework that jointly optimizes scaffolding and solution rollouts for code tasks. Supports text-generation and image-text-to-text pipelines. Context length unknown. Distributed as safetensors format, compatible with transformers ecosystem.
Run Ornith-1.0-35B 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-1.0-35B")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: ~70–140 GB VRAM for fp32 inference depending on MoE sparsity activation. Realistically, bf16/fp16 quantization (~35–70 GB) or GPTQ/AWQ quantization (~18–25 GB) typical for single-GPU deployment. Exact sparsity and activation patterns not documented—requires benchmarking. Model card claims single-GPU deployment feasibility but does not specify GPU tier or memory.
Not documented. LoRA/QLoRA feasibility unknown. Given MoE architecture and RL-based training approach, standard fine-tuning guidance insufficient. Requires custom exploration or guidance from deepreinforce-ai.
When to avoid it — and what to weigh
- Long-Context or Document-Heavy Tasks — Context length is unknown and not documented. If you need extended context (>8k tokens), verify feasibility before relying on this model.
- General-Purpose Language Understanding at Scale — Model is specialized for coding and agentic tasks. General NLP benchmarks (MMLU, etc.) are not provided. Use general-purpose alternatives if broad language capability is primary requirement.
- Real-Time, Sub-Second Latency Requirements — 35B MoE parameters require significant compute. Inference latency not documented. For ultra-low latency, benchmark against smaller models or quantized versions.
- Regulated Industries Without Security Review — No security audit or compliance documentation provided. Pre-deployment security review required for healthcare, finance, or other regulated sectors.
License & commercial use
MIT License. Permissive OSI-approved license allowing modification, distribution, and commercial use without restriction, provided original license is retained.
MIT license explicitly permits commercial use. No gating, regional restrictions, or additional commercial terms stated. Can be deployed in production and monetized. However, verify that deepreinforce-ai has not published separate commercial terms or usage policies outside the license file.
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 | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, adversarial robustness testing, or safety evaluation documented. Code generation models inherit risks: generated code may contain vulnerabilities or introduce supply-chain issues if deployed without review. Recommend: (1) validate generated code before execution; (2) run in sandboxed environments; (3) audit training data provenance (not disclosed); (4) test for prompt injection and jailbreaks in agentic context.
Alternatives to consider
Qwen 3.5-35B (base model)
Parent model; likely more stable but not optimized for agentic coding. Compare benchmarks directly if general-purpose performance needed.
Gemma 4-31B
Similar size, different architecture. Benchmarks show variable performance (lower on SWE-Bench Verified at 52%, but open-source alternative).
Claude 3.5 Sonnet (proprietary API)
Closes performance gap on coding benchmarks but requires external API, vendor lock-in, and per-token pricing. Consider if latency/cost trade-offs favor managed service.
Ship Ornith-1.0-35B with senior software developers
Benchmark Ornith-1.0-35B on your code tasks and infrastructure. Contact our team to discuss integration into private LLM or custom AI application workflows.
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Ornith-1.0-35B FAQ
Can I use Ornith-1.0-35B commercially?
What GPU should I use to run this model?
What is the context length?
Can I fine-tune this model?
Software development & web development with DEV.co
Adopting Ornith-1.0-35B is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Deploy Ornith for Autonomous Coding?
Benchmark Ornith-1.0-35B on your code tasks and infrastructure. Contact our team to discuss integration into private LLM or custom AI application workflows.