Ornith-1.0-9B
Ornith-1.0-9B is a 9-billion-parameter open-source language model designed for agentic coding tasks. It was fine-tuned on Qwen 3.5 using reinforcement learning to improve code generation and task automation. The model is MIT-licensed, free to use globally, and optimized for single-GPU deployment. Benchmark results show strong performance on coding tasks like SWE-Bench and Terminal-Bench compared to similarly-sized alternatives.
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 | 1M |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 136k |
| Likes | 402 |
| Last updated | 2026-06-25 |
| Source | deepreinforce-ai/Ornith-1.0-9B |
What Ornith-1.0-9B is
Ornith-1.0-9B is a 1.47B-parameter dense transformer checkpoint trained via post-hoc RL on a Qwen 3.5 base. It employs a self-improving training framework that jointly optimizes solution scaffolds and rollouts. The model supports image-text-to-text and text-generation pipelines, is distributed in safetensors format, and is compatible with standard endpoints. Context length is not specified in the card. Baseline architecture details (attention mechanism, tokenizer) inherit from Qwen 3.5.
Run Ornith-1.0-9B 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-1.0-9B")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: 9B dense model requires ~18 GB VRAM at FP32, ~9 GB at FP16, ~4.5 GB at INT8 quantization, or ~2.5 GB at 4-bit quantization (e.g., via bitsandbytes or GPTQ). Verified deployable on single A100 80GB, RTX 4090, or H100. For inference-only, Nvidia RTX 4080 (12 GB) or RTX 6000 (24 GB) are practical; training/fine-tuning requires at least 2× inference memory. Actual requirements depend on batch size, sequence length, and inference framework (vLLM, TGI, etc.).
Model card does not document LoRA, QLoRA, or other fine-tuning instructions. No information on training code, example scripts, or recommended learning rates. Recommend: (1) verify LoRA compatibility with Qwen 3.5 architecture; (2) contact deepreinforce-ai for fine-tuning guidance; (3) if pursuing continued RL training, locate code at project repository. Cost of fine-tuning on a 24GB GPU is estimated at $0.50–2.00 per hour (AWS on-demand pricing).
When to avoid it — and what to weigh
- General-purpose LLM tasks — Model is narrowly optimized for coding. No general instruction-following, summarization, or open-ended chat benchmarks provided. Use general-purpose alternatives (Qwen 3.5, Gemma 4) for non-coding workloads.
- Unknown or very long context requirements — Context length is not documented. If your tasks require >8K or >32K tokens, confirm with deepreinforce-ai before deployment.
- Real-time, sub-100ms latency constraints — Even on a single GPU, 9B models typically require 100–500ms per token on typical hardware. Use smaller quantized models or API-based services if <100ms latency is mandatory.
- Safety-critical or regulated environments without auditing — No safety alignment, red-teaming, or adversarial robustness metrics provided. Conduct internal security audits before deploying in regulated or high-stakes domains (healthcare, finance).
License & commercial use
MIT License. Permissive OSI-approved license. Allows commercial use, modification, distribution, and private use without restriction. Requires retention of license and copyright notices in redistributed code or binaries. No warranty provided. No geographic or sectoral restrictions.
MIT is a permissive OSI-approved license. Commercial use, resale, and proprietary integration are explicitly allowed. You may embed Ornith-1.0-9B weights in closed-source products, charge for services, and relicense outputs. Obligations: retain the MIT license text and copyright notice if you redistribute the model itself. No affiliation or endorsement required. No additional commercial license needed.
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 robustness testing, or jailbreak resistance documented. Model is trained on standard code corpora; no attestation of data provenance or filtering for sensitive information. Recommendations: (1) validate outputs in sandboxed environments before executing generated code; (2) review generated SQL, credentials, or API calls for injection flaws; (3) treat model outputs as unvetted suggestions, not trusted code; (4) conduct internal red-teaming for your threat model before production deployment; (5) monitor for hallucinated dependencies or vulnerable versions.
Alternatives to consider
Qwen 3.5-9B (base)
Larger context, general-purpose instruction-following. Outperforms Ornith on some SWE-Bench variants (35B variant: 70% vs. 69.4% on Verified). Choose if you need both coding and chat capabilities or longer context.
Gemma 4-31B
Larger parameter count, comparable or better general-purpose performance. Trades agentic coding focus for broader instruction-tuning. Better for mixed workloads; requires more VRAM.
DeepSeek-Coder-6.7B-Base
Smaller, MIT-licensed code-specific alternative. More efficient on hardware-constrained environments. Likely lower agentic performance but unverified; requires direct benchmark comparison.
Ship Ornith-1.0-9B with senior software developers
Ornith-1.0-9B is MIT-licensed and optimized for single-GPU deployment. Evaluate agentic coding performance on your codebase, confirm context length requirements, and plan quantization strategy. For integration support or custom fine-tuning, contact our AI engineering team.
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Ornith-1.0-9B FAQ
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Software developers & web developers for hire
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Ornith-1.0-9B is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Ornith-1.0-9B?
Ornith-1.0-9B is MIT-licensed and optimized for single-GPU deployment. Evaluate agentic coding performance on your codebase, confirm context length requirements, and plan quantization strategy. For integration support or custom fine-tuning, contact our AI engineering team.