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mudler merged 1 commit into from
Jun 28, 2026
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chore(model-gallery): ⬆️ update checksum #10585

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81 changes: 4 additions & 77 deletions gallery/index.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,26 +3,7 @@
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
urls:
- https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF
description: |
# 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

...
description: "# Qwen-AgentWorld-35B-A3B\n\n\U0001F4D1 Technical Report |\n\U0001F4D6 Blog |\n\U0001F917 Hugging Face |\n\U0001F916 ModelScope |\n\U0001F4BB GitHub |\n\U0001F5A5️ Demo\n\n> [!Note]\n> This repository contains the model weights and configuration files for **Qwen-AgentWorld-35B-A3B**, a native language world model trained for agentic environment simulation.\n>\n> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.\n\n**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.\n\n## Highlights\n\n...\n"
license: "apache-2.0"
tags:
- llm
Expand Down Expand Up @@ -51,34 +32,7 @@
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
urls:
- https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B-GGUF
description: |
[](https://deep-reinforce.com/ornith.html)

# Ornith-1.0-9B-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 9B

This model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.

### Benchmarks

Ornith-1.0-9B
Qwen3.5-9B
Qwen3.5-35B
Gemma4-12B
Gemma4-31B

Agentic Coding

...
description: "[](https://deep-reinforce.com/ornith.html)\n\n# Ornith-1.0-9B-GGUF\n\nAloha! \U0001F33A Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.\n\nHighlights:\n\n - **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.\n - **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.\n - **Licence**: MIT licensed, globally accessible, and free from regional limitations.\n\n## Ornith 1.0 9B\n\nThis model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.\n\n### Benchmarks\n\nOrnith-1.0-9B\nQwen3.5-9B\nQwen3.5-35B\nGemma4-12B\nGemma4-31B\n\nAgentic Coding\n\n...\n"
license: "mit"
tags:
- llm
Expand All @@ -105,34 +59,7 @@
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
urls:
- https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF
description: |
[](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

...
description: "[](https://deep-reinforce.com/ornith.html)\n\n# Ornith-1.0-35B-GGUF\n\nAloha! \U0001F33A Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.\n\nHighlights:\n\n - **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.\n - **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.\n - **Licence**: MIT licensed, globally accessible, and free from regional limitations.\n\n## Ornith 1.0 35B\n\nThis model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.\n\n### Benchmarks\n\nOrnith-1.0-35B\nQwen3.5-35B\nQwen3.6-35B\nGemma4-31B\nQwen3.5-397B\n\nAgentic Coding\n\n...\n"
license: "mit"
tags:
- llm
Expand Down Expand Up @@ -473,8 +400,8 @@
use_tokenizer_template: true
files:
- filename: llama-cpp/models/Qwythos-9B-Claude-Mythos-5-1M-GGUF/Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf
sha256: 24ee22e0f5d9f0d3d615809607f365c728d9b0c3f3fb6eb19d8bd83a1c2933d8
uri: https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF/resolve/main/Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf
sha256: 671c430bf18c961251338d639a3c02aac7451c39eed25874cad74287ac6cd38a
- filename: llama-cpp/mmproj/Qwythos-9B-Claude-Mythos-5-1M-GGUF/mmproj-Qwythos-9B-Claude-Mythos-5-1M-f16.gguf
sha256: f70dc3509053962b0d0d3ee8a7eacebf5d60aa560cad78254ae8698516ae029f
uri: https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF/resolve/main/mmproj-Qwythos-9B-Claude-Mythos-5-1M-f16.gguf
Expand Down
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