Ornith-1.0-35B-MTP-APEX-GGUF
Ornith-1.0-35B-MTP-APEX-GGUF is a 35B-parameter mixture-of-experts LLM optimized for agentic coding tasks. It is derived from Qwen3.5 and trained with reinforcement learning for code generation and reasoning. The GGUF quantization makes it suitable for local/on-premise deployment. MIT license permits commercial use without restrictions. Not gated.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | SC117 |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 47.5k |
| Likes | 45 |
| Last updated | 2026-07-02 |
| Source | SC117/Ornith-1.0-35B-MTP-APEX-GGUF |
What Ornith-1.0-35B-MTP-APEX-GGUF is
Qwen3.5-based MoE model with 256 routed experts (3B active per token), 40 transformer layers + 1 MTP layer, 262k context window, and integrated vision projector (mmproj) for multimodal capabilities. Distributed as APEX-quantized GGUFs optimized for llama.cpp. MTP layer sourced from Qwen3.5-35B-A3B. Benchmarked on Terminal-Bench 2.1, SWE-Bench Verified/Pro/Multilingual, NL2Repo, and OpenClaw.
Run Ornith-1.0-35B-MTP-APEX-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="SC117/Ornith-1.0-35B-MTP-APEX-GGUF")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: APEX-I-Compact quantization ~15.85 GB (per model card). Full precision likely 140+ GB. For local inference: minimum 16 GB GPU VRAM (RTX 4060 Ti / A6000 range) or 32+ GB CPU RAM. Benchmarks reference RTX 5070 Ti. Throughput and latency scale with hardware; consumer GPUs will see ~1–10 tokens/sec depending on quantization and batch size. Verify VRAM headroom by testing on target hardware.
Not documented in card. Model is post-trained on RL; no mention of LoRA/QLoRA compatibility or fine-tuning recommendations. If fine-tuning is required, consult base model (Ornith-1.0-35B) or Qwen3.5 documentation. Quantized GGUFs are typically not fine-tuned; dequantize or obtain unquantized weights if adaptation is needed.
When to avoid it — and what to weigh
- Latency-Critical Real-Time Systems — GGUF inference on consumer GPUs/CPUs will exhibit higher latency than commercial API endpoints. 35B parameters (even with MoE sparsity) demand significant compute. Not suitable for sub-100ms response SLAs.
- Production Deployments Without Infrastructure — While GGUF simplifies local serving, production use still requires GPU hardware (RTX 5070 Ti+ implied from benchmarks), vLLM/TGI setup, monitoring, and failover planning. Not a turnkey solution.
- Need for Domain Specialization via Fine-Tuning — No LoRA/QLoRA guidance documented. Model is post-trained and likely not optimized for further fine-tuning. If your domain requires significant adaptation, alternatives with explicit fine-tuning support may be preferable.
- Minimal Hardware Constraints — APEX-I-Compact is 15.85 GB; even quantized, this exceeds mobile, edge, or very-low-memory deployments. Requires dedicated GPU VRAM or substantial CPU memory.
License & commercial use
MIT License. Permissive, open-source. Allows commercial use, modification, and distribution with attribution. No copyleft or proprietary restrictions.
MIT is a permissive OSI-approved license. Commercial use is explicitly permitted without restrictions. No dual licensing, no special commercial terms, no gating. You may deploy, modify, and sell products built on this model. Attribution required. No liability warranty—typical for open-source.
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 |
GGUF quantization reduces attack surface vs. full-precision weights. Local deployment (llama.cpp) avoids cloud data transmission. No security audit mentioned. Standard LLM risks apply: prompt injection, model extraction (if served over network), and resource exhaustion (DoS via large context requests). Validate input sizes and implement rate-limiting if exposed. No built-in content filtering noted.
Alternatives to consider
Qwen3.5 (base model, unquantized)
Original Qwen3.5 MoE backbone. Offers more flexibility for fine-tuning and integration with ecosystem tools. Less optimized for on-prem GGUF inference; requires cloud or high-end hardware.
DeepSeek-Coder v2 (236B, open-source)
Larger open-source coding model with strong SWE-Bench performance. More parameters but also higher resource demand. Different licensing and deployment profile.
Llama 3.1 (70B/8B variants, Meta)
Broader generalist model with extensive community support and documentation. Lower coding specialization but wider ecosystem integration (vLLM, TGI, Ollama). MIT license.
Ship Ornith-1.0-35B-MTP-APEX-GGUF with senior software developers
Evaluate this open-source agentic coding model on your infrastructure. GGUF quantization and llama.cpp support enable zero-cloud-dependency deployment. MIT license permits commercial use. Start with APEX-I-Compact (15.85 GB) and scale with our guidance on hardware, serving, and integration.
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Ornith-1.0-35B-MTP-APEX-GGUF FAQ
Can I use this model commercially?
How much GPU memory do I need to run Ornith-1.0-35B-MTP-APEX locally?
Does this model support fine-tuning?
What is MTP and how does it affect performance?
Custom software development services
Need help beyond evaluating Ornith-1.0-35B-MTP-APEX-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Deploy Ornith-1.0-35B Locally for Secure Code Workflows
Evaluate this open-source agentic coding model on your infrastructure. GGUF quantization and llama.cpp support enable zero-cloud-dependency deployment. MIT license permits commercial use. Start with APEX-I-Compact (15.85 GB) and scale with our guidance on hardware, serving, and integration.