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ornith-1.5-35b-a3b-q4
Ornith-1.5-35B-A3B is an MIT-licensed Qwen3.5 mixture-of-experts model
from Ornith AI for agentic coding, reasoning, repository-level software
tasks, and tool use. It activates about 3B parameters per token and
supports text and image input with a context window of 262K tokens.
This default entry uses the Q4_K_M GGUF and BF16 vision projector. A
higher-quality Q8_0 model is available as a variant.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-q8
Ornith-1.5-35B-A3B in the higher-quality Q8_0 GGUF format, with the shared
BF16 vision projector for multimodal prompts.
Repository: localaiLicense: mit
tiel-coder-35b-a3b-q4
Tiel-Coder-35B-A3B is a 35B-parameter mixture-of-experts model for coding,
reasoning, tool use, and vision tasks. This default entry uses the
Q4_K_XL GGUF and BF16 vision projector.
Repository: localaiLicense: mit
tiel-coder-35b-a3b-q4-mtp
Tiel-Coder-35B-A3B in Q4_K_XL format with MTP speculative decoding and a
BF16 vision projector.
Repository: localaiLicense: mit
tiel-coder-35b-a3b-q8
Tiel-Coder-35B-A3B in the higher-quality Q8_K_XL GGUF format, with the
BF16 vision projector for multimodal prompts.
Repository: localaiLicense: mit
pocket-35b
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the quality-oriented Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-q3
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the balanced Q3_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-q2
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the smaller Q2_K GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-iq1
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the most compact IQ1_M GGUF quantization.
Repository: localaiLicense: apache-2.0
mellum2-12b-a2.5b-instruct
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
mellum2-12b-a2.5b-instruct-q8
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-uncensored-genesis-hermes-v6
Qwen3.6-35B-A3B Uncensored Genesis Hermes V6 is LuffyTheFox's multimodal,
agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
combines Genesis tensor calibration with Hermes function-calling data while
retaining the 35B mixture-of-experts architecture, roughly 3B active
parameters per token, and the native 262K-token context window.
This entry installs the Q8_0 GGUF together with its F16 multimodal projector
for llama.cpp. The model card recommends Jinja chat templates and at least a
128K context for its thinking behavior. License: Apache-2.0.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7
Qwen3.6-35B-A3B Genesis Hermes V7 is LuffyTheFox's Apache-2.0 multimodal,
agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
combines Genesis tensor calibration with Hermes function-calling data while
retaining the 35B mixture-of-experts architecture, roughly 3B active
parameters per token, and the native 262K-token context window.
This entry's own payload uses the model card's recommended APEX GGUF and the
shared F16 multimodal projector. Automatic variant selection may instead
choose Compact APEX, an MTP-enabled APEX build, or Q8_K_P based on serving
features and available memory. The model card recommends Jinja chat
templates and at least a 128K context for its thinking behavior.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7-apex-compact
Qwen3.6-35B-A3B Genesis Hermes V7 in the smaller APEX Compact GGUF format,
with the shared F16 multimodal projector. This build preserves the model's
multimodal, reasoning, coding, and agentic capabilities for hosts with less
memory than the recommended full APEX build.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7-mtp-apex
Qwen3.6-35B-A3B Genesis Hermes V7 in the full APEX GGUF format with native
multi-token prediction enabled for speculative decoding, plus the shared
F16 multimodal projector.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7-mtp-apex-compact
Qwen3.6-35B-A3B Genesis Hermes V7 in the smaller APEX Compact GGUF format
with native multi-token prediction enabled for speculative decoding, plus
the shared F16 multimodal projector.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7-q8-k-p
Qwen3.6-35B-A3B Genesis Hermes V7 in the high-quality Q8_K_P GGUF format,
with the shared F16 multimodal projector. This is the largest non-MTP build
in the published V7 set.
Repository: localaiLicense: apache-2.0
qwopus3.6-35b-a3b-coder-mtp
# 🌟 Qwopus3.6-35B-A3B-v1
## 💡 Base Model Overview
**Qwen3.6-35B-A3B** is an advanced hybrid sparse MoE (Mixture-of-Experts) model developed by Alibaba Cloud. It features 35B total parameters with only 3B active parameters per token, ensuring high inference efficiency. Architecturally, it combines Gated DeltaNet linear attention with standard gated attention layers, routing tokens across **256 experts**. It natively supports a massive **262k context window** and is specifically designed for high-performance agentic coding, deep reasoning, and multimodal tasks.
## 🚀 Model Refinement & Logic Tuning （Qwopus3.6-35B-A3B-v1）
🪐**Qwopus3.6-35B-A3B-v1** is a reasoning-enhanced MoE (Mixture of Experts) model fine-tuned on top of **Qwen3.6-35B-A3B**.
### 🛠 Training Strategy
The fine-tuning process for this model is structured into **three distinct stages of distributed SFT (Supervised Fine-Tuning)**, progressively scaling reasoning complexity and data diversity. This systematic approach ensures the model inherits the base MoE capabilities while sharpening its logic-handling depth.
...
Repository: localaiLicense: apache-2.0
qwen-agentworld-35b-a3b
# Qwen-AgentWorld-35B-A3B
📑 Technical Report |
📖 Blog |
🤗 Hugging Face |
🤖 ModelScope |
💻 GitHub |
🖥️ Demo
> [!Note]
> This repository contains the model weights and configuration files for **Qwen-AgentWorld-35B-A3B**, a native language world model trained for agentic environment simulation.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.
**Qwen-AgentWorld** is the first language world model to cover seven agent interaction domains within a single model. It simulates agentic environments via long chain-of-thought reasoning, predicting the next environment state given an agent's action and interaction history. Trained through a three-stage pipeline — CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity — Qwen-AgentWorld is a **native world model**: environment modeling is the training objective from the CPT stage onward, not a post-hoc add-on.
## Highlights
...
Repository: localaiLicense: apache-2.0
ornith-1.0-35b
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-35B-GGUF
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 35B
This model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-35B
Qwen3.5-35B
Qwen3.6-35B
Gemma4-31B
Qwen3.5-397B
Agentic Coding
...
Repository: localaiLicense: mit
qwen3.6-35b-a3b-dflash
Qwen3.6-35B-A3B (Mixture-of-Experts, ~3B active per token) paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash speedups on MoE targets are smaller than on dense models, but still useful.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The UD-Q4_K_M file carries the full Qwen3.6-35B-A3B target; the ~0.4 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
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