Ornith-1.0-35B-FP8
Ornith-1.0-35B is a 35-billion-parameter open-source LLM optimized for agentic coding tasks. It is post-trained on Qwen 3.5 and uses a mixture-of-experts (MoE) architecture. The model is MIT-licensed, ungated, and designed for single-GPU deployment. It reports strong performance on coding benchmarks (SWE-Bench, Terminal-Bench, NL2Repo) relative to comparable-sized open models.
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 | 35.1B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 119.8k |
| Likes | 65 |
| Last updated | 2026-06-26 |
| Source | deepreinforce-ai/Ornith-1.0-35B-FP8 |
What Ornith-1.0-35B-FP8 is
Ornith-1.0-35B is a 35B-parameter MoE model built on Qwen 3.5. It employs reinforcement learning to jointly optimize solution scaffolds and rollouts for code generation. The model is quantized to FP8 precision, available in SafeTensors format, and tagged as endpoints-compatible. Last modified June 2026. Downloads: 119,830; Likes: 65. Context length and inference latency are not documented.
Run Ornith-1.0-35B-FP8 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-FP8")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 (requires verification): FP8 quantization of 35B parameters ≈ ~70–80 GB raw model size. Single-GPU deployment suggested by model card implies targeting H100/A100 or equivalent (~80GB VRAM). Running on smaller GPUs (RTX 4090, 48GB) would likely require further quantization (INT4/INT8 via LoRA or distillation) or offloading. No official specs provided; benchmark actual inference on target hardware.
Model card does not explicitly discuss fine-tuning, LoRA, or QLoRA feasibility. Given the MoE architecture and RL-based post-training, efficient adaptation is plausible but not confirmed. LoRA on a 35B MoE model is more complex than dense models; QLoRA may be viable on 24–48GB GPUs. Recommend consulting deepreinforce-ai documentation or running proof-of-concept before committing to large-scale fine-tuning.
When to avoid it — and what to weigh
- Requirement for Very Long Context (>32K tokens) — Context length is not documented. If your use case requires extended reasoning windows or large codebase analysis, context limitations are unknown and must be tested.
- Need for General-Purpose Conversation — Model is specialized for coding and agentic tasks. Performance on general QA, creative writing, or non-technical domains is not benchmarked and likely inferior to generalist models.
- Real-Time, Sub-100ms Latency Requirement — 35B parameter count and MoE architecture trade computational cost for quality. Inference speed and serving overhead are not disclosed; verify latency expectations against measured throughput.
- Requirement for Proven Production Stability at Scale — Recent release (June 2026), moderate download count (119k), and no disclosed production deployment references. Suitable for early adoption or controlled pilots; not yet battle-tested in large production systems.
License & commercial use
MIT license. Permissive, OSI-compliant open-source license allowing commercial use, modification, and redistribution with attribution.
MIT license explicitly permits commercial use. No gating, no regional restrictions, no additional terms stated. Model weights and derivatives can be integrated into proprietary products. However: (1) verify terms of underlying base models (Qwen 3.5, Gemma 4) if derivatives are used; (2) consult legal counsel before deploying in regulated domains (healthcare, finance, etc.) due to lack of documented safety testing and compliance vetting.
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 explicit security audit, adversarial testing, or safety training reported. Model is trained on public code datasets (SWE-Bench, Terminal-Bench); risk of embedding known vulnerabilities or malicious patterns in training data is not assessed. FP8 quantization may introduce minor numerical drift; impact on model behavior is untested. Use in security-critical or safety-sensitive contexts requires independent red-teaming and validation.
Alternatives to consider
Qwen3.5-35B (base, non-specialized)
Same parameter count and architecture, but not fine-tuned for coding. Offers flexibility for general tasks but lacks agentic optimization and benchmark parity on code tasks.
DeepSeek-Coder-33B or similar
Comparable size, also optimized for code. May offer different trade-offs in inference speed, licensing terms, or benchmark coverage; requires comparison testing.
Gemma 4 31B (base)
Slightly smaller, also recent. Offers different licensing (to verify) and architectural choices; benchmark results shown in card suggest Ornith-1.0-35B outperforms on most metrics but Gemma 4 may better suit other workflows.
Ship Ornith-1.0-35B-FP8 with senior software developers
Ornith-1.0-35B offers MIT-licensed, agentic coding capability with proven benchmarks on real-world tasks. Start with a single-GPU pilot, test context length and latency on your hardware, and validate security posture before production rollout. Contact Devco for architecture guidance or custom fine-tuning.
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Ornith-1.0-35B-FP8 FAQ
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Ready to Deploy a Coding LLM?
Ornith-1.0-35B offers MIT-licensed, agentic coding capability with proven benchmarks on real-world tasks. Start with a single-GPU pilot, test context length and latency on your hardware, and validate security posture before production rollout. Contact Devco for architecture guidance or custom fine-tuning.