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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
qwopus3.6-27b-coder-mtp
🪐 Qwopus3.6-27B-v2
SFT Release
Reasoning-Enhanced Dense Language Model Fine-Tuned on Qwen3.6-27B
🧬 Trace Inversion & Negentropy
🧠 27B Parameters
🔥 3-Stage Curriculum SFT
🛠️ Vision & Tool-use Support
💡 What is Qwopus3.6-27B-v2?
🪐 Qwopus3.6-27B-v2 is a reasoning-enhanced dense language model built on top of Qwen3.6-27B. By leveraging a multi-stage curriculum learning pipeline and augmented with Trace Inversion datasets (claude-opus-4.6/4.7-traceInversion), it reverse-engineers the compressed "Reasoning Bubbles" of commercial LLMs into structured, step-by-step synthetic reasoning traces, successfully eliminating logical shortcuts and knowledge fractures.
🧩 Structured Reasoning
Injects reconstructed deep CoT chains to eliminate logical shortcuts via Trace Inversion.
🪶 Style Consistency
Enforces strict constraints on the format and convergence of <think> tags.
🔁 Distillation Alignment
Ensures high-quality cross-source SFT data alignment to narrow the capacity gap.
⚡ RL Scalability
Sets up a stable formatting pipeline optimized for downstream Reinforcement Learning (RL).
## 💡 1. Base Model, Training Library & Cooperation
...
Repository: localaiLicense: apache-2.0
gemma-4-26b-a4b-it-qat
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model.
> Four versions of the QAT checkpoints are available:
> * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
> * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
> * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
> * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B
...
Repository: localaiLicense: apache-2.0
gemma-4-12b-it-qat-q4_0
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model.
> Four versions of the QAT checkpoints are available:
> * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
> * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
> * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
> * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B
...
Repository: localaiLicense: apache-2.0
step-3.7-flash
**[ModelPage]**: https://static.stepfun.com/blog/step-3.7-flash/
## 1. Introduction
Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model that combines a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding. Engineered for high-frequency production workloads, it activates approximately 11B parameters per token and delivers a throughput of up to 400 tokens per second. Step 3.7 Flash supports a 256k context window and offers three selectable reasoning levels (low, medium, and high) so developers can easily balance speed, cost, and cognitive depth.
We built Step 3.7 Flash for developers who need to scale agentic workflows that combine perception, search, and reasoning. It is designed to handle intensive tasks such as parsing massive financial reports in one pass, running multi-step search loops with cross-source verification, or operating concurrent coding agents in high-throughput pipelines.
## 2. Capabilities & Performance
### Multimodal Perception and Verification
...
Repository: localaiLicense: apache-2.0
silero-vad-sherpa
Silero VAD served through the sherpa-onnx backend. Uses the same ONNX weights as the dedicated silero-vad backend, loaded through sherpa-onnx's C VAD API. Pairs with the sherpa-onnx ASR entries for round-trip audio pipelines.
Repository: localaiLicense: mit
vits-ljs-sherpa
VITS-LJS English single-speaker TTS served through the sherpa-onnx backend. Trained on the LJSpeech corpus at 22.05 kHz. Pairs with the sherpa-onnx ASR entries for round-trip audio pipelines.
Repository: localaiLicense: apache-2.0
acestep-cpp-turbo
ACE-Step 1.5 Turbo (C++ / GGML) — native C++ music generation from text descriptions and lyrics.
Two-stage pipeline: text-to-code (Qwen3 LM) + code-to-audio (DiT-VAE). Stereo 48kHz output.
Uses Q8_0 quantized models for a good balance of quality and speed.
Repository: localaiLicense: mit
vits-streaming-assistant
English voice assistant preset: Silero VAD, Parakeet TDT 0.6B v3, Qwen3
0.6B chat, and VITS-LJS English single-speaker TTS through sherpa-onnx.
Buffers streamed LLM output into clauses and emits audio progressively.
Each clause starts a separate synthesis; this is not native incremental
text input. This installs only the pipeline configuration. First install
silero-vad-ggml, parakeet-cpp-tdt-0.6b-v3, qwen3-0.6b, and vits-ljs-sherpa.
WebRTC also needs the opus backend. No browser or hardware performance
is established by this preset.
Repository: localaiLicense: mit
piper-streaming-assistant
English voice assistant preset: Silero VAD, Parakeet TDT 0.6B v3, Qwen3
0.6B chat, and Piper Amy US English TTS through piper.
Buffers streamed LLM output into clauses and emits audio progressively.
Each clause starts a separate synthesis; this is not native incremental
text input. This installs only the pipeline configuration. First install
silero-vad-ggml, parakeet-cpp-tdt-0.6b-v3, qwen3-0.6b, and voice-en-us-amy-low.
WebRTC also needs the opus backend. No browser or hardware performance
is established by this preset.
Repository: localaiLicense: mit
qwen3-streaming-assistant
English voice assistant preset: Silero VAD, Parakeet TDT 0.6B v3, Qwen3
0.6B chat, and Qwen3-TTS 0.6B CustomVoice with Ryan's voice. Streams LLM
text into one persistent TTS session while audio is emitted.
This installs only the pipeline configuration. First install the separate
gallery models silero-vad-ggml, parakeet-cpp-tdt-0.6b-v3, qwen3-0.6b, and
qwen3-tts-cpp-customvoice. Pin its variant to qwen3-tts-cpp-customvoice
for Q8. WebRTC also needs the opus backend.
Requires a LocalAI build with incremental Qwen3-TTS input support.
CPU synthesis may be slower than playback; GPU and browser performance
are not established by this preset.
Repository: localaiLicense: mit
deepseek-ai.deepseek-v3.2
This is a quantized version of the DeepSeek-V3.2 model by deepseek-ai, optimized for efficient deployment. It is designed for text generation tasks and supports the pipeline tag `text-generation`. The model is based on the original DeepSeek-V3.2 architecture and is available for use in various applications. For more details, refer to the [official repository](https://github.com/DevQuasar/deepseek-ai.DeepSeek-V3.2-GGUF).
Repository: localai
fish-speech-s2-pro
Fish Speech S2-Pro is a high-quality text-to-speech model supporting voice cloning via reference audio. Uses a two-stage pipeline: text to semantic tokens (LLaMA-based) then semantic to audio (DAC decoder).
Repository: localaiLicense: fish-audio-research-license
qwen3-vl-reranker-8b
**Model Name:** Qwen3-VL-Reranker-8B
**Base Model:** Qwen/Qwen3-VL-Reranker-8B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-8B-GGUF) (e.g., `Qwen3-VL-Reranker-8B.Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
qwen3-vl-reranker-2b-i1
**Model Name:** Qwen3-VL-Reranker-2B-i1
**Base Model:** Qwen/Qwen3-VL-Reranker-2B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-2B-i1-GGUF) (e.g., `Qwen3-VL-Reranker-2B.i1-Q4_K_M.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
lfm3-audio-2b-vad
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector). An any-to-any model: one llama.cpp session takes speech or text
and answers with speech, text or both, and the same model also transcribes speech (ASR, with a language
hint) and synthesises speech with the named voices female or male.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you). The VAD only decides when a turn
ends; the model itself answers the speech.
Speech-to-speech over /v1/realtime is experimental and needs a LocalAI build whose llama.cpp pin includes the
LFM3 audio and realtime support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8-vad
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector). An any-to-any model: one llama.cpp session takes speech or text
and answers with speech, text or both, and the same model also transcribes speech (ASR, with a language
hint) and synthesises speech with the named voices female or male.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you). The VAD only decides when a turn
ends; the model itself answers the speech.
Speech-to-speech over /v1/realtime is experimental and needs a LocalAI build whose llama.cpp pin includes the
LFM3 audio and realtime support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-pipeline
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
No VAD model is set: the client commits each turn with input_audio_buffer.commit. Use the
-pipeline-vad entry for automatic turn detection.
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8-pipeline
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
No VAD model is set: the client commits each turn with input_audio_buffer.commit. Use the
-pipeline-vad entry for automatic turn detection.
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-pipeline-vad
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you).
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8-pipeline-vad
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you).
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
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