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qwopus3.6-27b-coder-compat-mtp
🪐 Qwopus-3.6-27B-Coder Coder SFT Release Agentic Coding & Tool-Use Reasoning Model Fine-Tuned on Qwopus3.6-27B-v2 🧬 Trace Inversion & Negentropy 🧠 27B Dense Model ⚡ Agentic Coding 🛠️ Tool Calling & Agent 🏆 SWE-bench Verified: 67.0% (off-thinking) 💡 What is Qwopus-3.6-27B-Coder? 🪐 Qwopus-3.6-27B-Coder is a reasoning-enhanced agentic coding model built on top of Qwopus3.6-27B-v2. It inherits the powerful reasoning foundation of the v2 base — which achieved 87.43% MMLU-Pro and 75.25% SWE-bench Verified — and further specializes it for agentic code generation, structured tool calling, debugging, and instruction-following in developer workflows. The model is designed to excel at repository-level coding tasks, multi-turn tool orchestration, and complex logical reasoning under realistic agent environments. 🧩 Agentic Coding Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows. 🛠️ Tool Calling Learns from real agent trajectories with tool definitions, tool calls, and environment feedback for robust multi-turn execution. ...

Repository: localaiLicense: apache-2.0

qwopus3.6-27b-coder-mtp-nvfp4
🪐 Qwopus-3.6-27B-Coder Coder SFT Release Agentic Coding & Tool-Use Reasoning Model Fine-Tuned on Qwopus3.6-27B-v2 🧬 Trace Inversion & Negentropy 🧠 27B Dense Model ⚡ Agentic Coding 🛠️ Tool Calling & Agent 🏆 SWE-bench Verified: 67.0% (off-thinking) 💡 What is Qwopus-3.6-27B-Coder? 🪐 Qwopus-3.6-27B-Coder is a reasoning-enhanced agentic coding model built on top of Qwopus3.6-27B-v2. It inherits the powerful reasoning foundation of the v2 base — which achieved 87.43% MMLU-Pro (300ex) and 75.25% SWE-bench Verified — and further specializes it for agentic code generation, structured tool calling, debugging, and instruction-following in developer workflows. The model is designed to excel at repository-level coding tasks, multi-turn tool orchestration, and complex logical reasoning under realistic agent environments. 🧩 Agentic Coding Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows. 🛠️ Tool Calling Learns from real agent trajectories with tool definitions, tool calls, and environment feedback for robust multi-turn execution. ...

Repository: localai

gemma-4-12b-agentic-fable5-composer2.5-v2-3.5x-tau2
Hugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. ...

Repository: localaiLicense: apache-2.0

gemma-4-12b-coder-fable5-composer2.5-v1
Hugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. ...

Repository: localaiLicense: gemma

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

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

qwopus3.5-9b-coder-mtp
# 🌟 Qwopus3.5-9B-v3.5 ## 💡 Model Overview & v3.5 Design Qwopus3.5-9B-v3.5 is a **data-scaled continuation** of the Qwopus3.5-9B-v3 model. The training data in v3.5 is expanded to cover a broader range of domains, including mathematics, programming, puzzle-solving, multilingual dialogue, instruction-following, multi-turn interactions, and STEM-related tasks. Qwopus3.5-9B-v3.5 is a reasoning-enhanced model based on **Qwen3.5-9B**, designed for: - 🧩 Structured reasoning - 🔧 Tool-augmented workflows - 🔁 Multi-step agentic tasks - ⚡ Token-efficient inference Compared with Qwopus3.5-9B-v3, **3.5 version does not introduce a new architecture, RL stage, or template redesign**. This version is trained with approximately **2× more SFT data**. ## 🎯 Motivation & Generalization Insight The motivation behind v3.5 comes from a simple observation: > This work is motivated by the hypothesis that scaling high-quality SFT data may further enhance the generalization ability of large language models. In earlier Qwopus3.5 experiments, structured reasoning was observed to improve both **accuracy and efficiency**: ...

Repository: localaiLicense: apache-2.0

qwen3.6-35b-a3b-claude-4.6-opus-reasoning-distilled
# 🔥 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled A reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving. The training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples. - **Developed by:** @hesamation - **Base model:** `Qwen/Qwen3.6-35B-A3B` - **License:** apache-2.0 This fine-tuning run is inspired by Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction. [](https://x.com/Hesamation) [](https://discord.gg/vtJykN3t) ## Benchmark Results The MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark. ...

Repository: localaiLicense: apache-2.0

qwen3-coder-next-mxfp4_moe
The model is a quantized version of **Qwen/Qwen3-Coder-Next** (base model) using the **MXFP4** quantization scheme. It is optimized for efficiency while retaining performance, suitable for deployment in applications requiring lightweight inference. The quantized version is tailored for specific tasks, with parameters like temperature=1.0 and top_p=0.95 recommended for generation.

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

lfm2.5-audio-1.5b-realtime
LFM2.5-Audio-1.5B is LiquidAI's any-to-any audio foundation model. The 1.2B LFM2.5 backbone plus a FastConformer audio encoder and an LFM2-based audio detokenizer give real-time speech-to-speech with text + audio output interleaved at 12.5 Hz / 24 kHz. This entry runs in S2S (speech-to-speech) mode and is the model the LocalAI realtime API any-to-any path consumes. Switch to ASR, TTS, or chat by picking the sibling gallery entries.

Repository: localaiLicense: LFM-Open-License-v1.0

iquest-coder-v1-40b-instruct-i1
The **IQuest-Coder-V1-40B-Instruct-i1-GGUF** is a quantized version of the original **IQuestLab/IQuest-Coder-V1-40B-Instruct** model, designed for efficient deployment. It is an **instruction-following large language model** with 40 billion parameters, optimized for tasks like code generation and reasoning. **Key Features:** - **Size:** 40B parameters (quantized for efficiency). - **Purpose:** Instruction-based coding and reasoning. - **Format:** GGUF (supports multi-part files). - **Quantization:** Uses advanced techniques (e.g., IQ3_M, Q4_K_M) for balance between performance and quality. **Available Quantizations:** - Optimized for speed and size: **i1-Q4_K_M** (recommended). - Lower-quality options for trade-off between size/quality. **Note:** This is a **quantized version** of the original model, but the base model (IQuestLab/IQuest-Coder-V1-40B-Instruct) is the official source. For full functionality, use the unquantized version or verify compatibility with your deployment tools.

Repository: localaiLicense: iquestcoder

qwen3-coder-30b-a3b-instruct-rtpurbo-i1
The model in question is a quantized version of the original **Qwen3-Coder** large language model, specifically tailored for code generation. The base model, **RTP-LLM/Qwen3-Coder-30B-A3B-Instruct-RTPurbo**, is a 30B-parameter variant optimized for instruction-following and code-related tasks. It employs the **A3B attention mechanism** and is trained on diverse data to excel in programming and logical reasoning. The current repository provides a quantized (compressed) version of this model, which is suitable for deployment on hardware with limited memory but loses some precision compared to the original. For a high-fidelity version, the unquantized base model is recommended.

Repository: localai

opengvlab_internvl3_5-30b-a3b
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-30b-a3b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-14b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-14b
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-8b
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-8b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-4b
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-4b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

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