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qwen3.5-9b-defiant-fable-mtp
Qwen3.5 9B Defiant Fable is an Apache-2.0 multimodal fine-tune for
reasoning, coding, creative writing, and roleplay. It retains the 256K
context window and vision support of Qwen3.5 while reducing refusals.
This default entry uses the NEO-imatrix Q4_K_M build with multi-token
prediction enabled for faster generation.
Repository: localaiLicense: apache-2.0
qwen3.5-9b-defiant-fable
Qwen3.5 9B Defiant Fable in the plain NEO-imatrix Q4_K_M GGUF format.
This fallback offers the same multimodal reasoning, coding, and creative
capabilities without enabling multi-token prediction.
Repository: localaiLicense: apache-2.0
btl-4-compact
BTL-4 Compact is Bad Theory Labs' text-only 35B mixture-of-experts model
compressed into a single 9.96 GB IQ2_XXS GGUF. Around 2.1B parameters are
active per token, and the model is tuned for agentic work, tool use, coding,
and reasoning. The compact build omits the vision tower and disables the
source model's MTP layer for compatibility with stock llama.cpp.
Repository: localaiLicense: apache-2.0
qwen3.5-9b-hauhaucs-aggressive
Qwen3.5 9B Aggressive is HauhauCS's refusal-removed fine-tune of the
multimodal Qwen3.5 9B model. It retains the base model's reasoning, tool
use, image and video understanding, and 262K-token native context window.
This entry uses the balanced Q4_K_M GGUF quantization and includes the
matching BF16 multimodal projector. The Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.5-9b-hauhaucs-aggressive-q8
Qwen3.5 9B Aggressive is HauhauCS's refusal-removed fine-tune of the
multimodal Qwen3.5 9B model. It retains the base model's reasoning, tool
use, image and video understanding, and 262K-token native context window.
This entry uses the higher-fidelity Q8_0 GGUF quantization and includes the
matching BF16 multimodal projector.
Repository: localaiLicense: apache-2.0
qwen3.5-4b-dflash
Qwen3.5-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend.
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 Q4_K_M file carries the full Qwen3.5-4B target; the ~0.6 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 (Qwen3.5-4B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
qwen3.5-9b-dflash
Qwen3.5-9B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend.
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 Q4_K_M file carries the full Qwen3.5-9B target; the ~1 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 (Qwen3.5-9B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
qwen3.5-9b-deepseek-v4-flash
# Qwen3.5-9B
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
## Qwen3.5 Highlights
Qwen3.5 features the following enhancement:
- **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
- **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
...
Repository: localaiLicense: apache-2.0
qwen3.5-9b-glm5.1-distill-v1
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwen3.5-35b-a3b-apex
Describe the model in a clear and concise way that can be shared in a model gallery.
Repository: localaiLicense: apache-2.0
arex-turbo
AREX-Turbo is BAAI's compact 4B deep-research agent, fine-tuned from
Qwen3.5-4B for long-horizon search, evidence aggregation, constraint
verification, and tool-assisted reasoning. It supports text and image
input with a 262K-token context window. This entry uses the recommended
Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
arex-turbo-q8
AREX-Turbo is BAAI's compact 4B deep-research agent, fine-tuned from
Qwen3.5-4B for long-horizon search, evidence aggregation, constraint
verification, and tool-assisted reasoning. It supports text and image
input with a 262K-token context window. This entry uses the higher-quality
Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
ui-mate-9b
UI-Mate-9B is Tencent's 9B-parameter multimodal computer-use agent,
fine-tuned from Qwen3.5-9B. It accepts task instructions, screenshots,
and interaction history, then emits reasoning and structured mouse and
keyboard actions for long-running desktop tasks. The model requires an
external runtime to execute its actions and should run with human
confirmation for sensitive operations. This entry uses the recommended
Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
ui-mate-9b-q8
UI-Mate-9B is Tencent's 9B-parameter multimodal computer-use agent,
fine-tuned from Qwen3.5-9B. It accepts task instructions, screenshots,
and interaction history, then emits reasoning and structured mouse and
keyboard actions for long-running desktop tasks. The model requires an
external runtime to execute its actions and should run with human
confirmation for sensitive operations. This entry uses the higher-quality
Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
fara1.5-4b
Fara1.5-4B is Microsoft's 4B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-4B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the recommended Q4_K_M GGUF quantization.
Repository: localaiLicense: mit
fara1.5-4b-q8
Fara1.5-4B is Microsoft's 4B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-4B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: mit
fara1.5-9b
Fara1.5-9B is Microsoft's 9B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-9B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the recommended Q4_K_M GGUF quantization.
Repository: localaiLicense: mit
fara1.5-9b-q8
Fara1.5-9B is Microsoft's 9B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-9B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: mit
fara1.5-27b
Fara1.5-27B is Microsoft's 27B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-27B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: mit
fara1.5-27b-q8
Fara1.5-27B is Microsoft's 27B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-27B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the Q8_0 GGUF quantization.
Repository: localaiLicense: mit
qwen_qwen3.5-35b-a3b
Qwen3.5-35B-A3B is a quantized multimodal language model with 35B parameters using an A3B MoE architecture. It supports image-text understanding and chat interactions via llama-cpp backend.
Repository: localaiLicense: apache-2.0
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