新闻与全站搜索
关联新闻、工具、模型、指南、专题、工作流与提示词。
AI 模型 56
“AI Code Guide”
Qwen3.8-27B
Developer Role Support so Qwen3.8 can work in agentic tools like Codex and more! Qwen3.8 can now be run and fine-tuned in Unsloth Desktop . Read our guide . Tool calling improvements: Makes parsing nested objects to make tool calling succeed more. See below for 1-bit Qwen3.8 run inside of Unsloth: Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability.
LLaDA-Image
Welcome to the official repository for LLaDA-Image, a unified model for high-quality image generation and editing. LLaDA-Image is a competitive 6B-parameter open-source unified image generation and editing model family. It includes **LLaDA-Image**, a 50-step Base model for high-quality text-to-image generation and instruction-guided editing, and **LLaDA-Image-Turbo**, a 4-step distilled model for fast generation and editing. Both variants support practical text-to-image generation, VQ-conditioned generation, reference-image editing, and Chinese--English text rendering. This repository provides the checkpoints and Diffusers-based inference code for the LLaDA-Image model family.
Arch-Router-1.5B
With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. We introduce a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. This model is described in the paper: https://arxiv.org/abs/2506.16655, and powers Arch the models-native proxy server for agents.
Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-v2-GGUF
🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab. **🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)** * **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face. * **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth. * *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*
GIST-all-MiniLM-L6-v2
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the sentence-transformers/all-MiniLM-L6-v2 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Llama-3.1-Tulu-3-8B-SFT
Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. SFT: - **Learning Rate**: 5E-6 (8B), 2E-6 (70B, 405B) - **Effective Batch Size:** 128 (8B, 70B), 256 (405B) - **Max. Sequence Length:** 4096 - **Loss Accumulation:** Sum (see https://unsloth.ai/blog/gradient) - **Learning Rate Schedule:** Linear - **LR Warmup Ratio:** 0.03 - **Num. Epochs:** 2
Qwopus3.5-9B-v3-GGUF
🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab. **🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)** * **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face. * **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth. * *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*
Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. > ❤️ Special thanks to the **Unsloth** open-source library and @KyleHessling1 for their support. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
qwen3-4b-heretic
An abliterated version of Qwen 3 4B created using Heretic v1.2.0. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for image generation models like Z-Image and FLUX.2 Klein 4B. Available in five ComfyUI-native quantized formats (FP8, INT8, INT4, NVFP4, MXFP8), all produced with SVD-guided learned rounding for maximum fidelity. > **Quality:** All quantized variants use SVD-guided learned rounding (AdaRound via convert_to_quant), which optimizes each weight's rounding direction to minimize output reconstruction error — noticeably higher fidelity than naive round-to-nearest quantization. All variants load natively in ComfyUI 0.30.0+ (no plugins) via the `comfy_quant` metadata embedded in each file.
Muse-Glimmer-30B-unsloth-bnb-4bit
See our Muse Glimmer guide for quantization analysis and instructions. You can now run Muse Glimmer in Unsloth with toggles for thinking. Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. The model integrates multi-step reasoning, reliable tool use, multimodal understanding, and failure recovery into a single model that runs locally without requiring cloud infrastructure or network access. Building effective agents requires key capabilities working together to achieve the user’s goals. Muse Glimmer is trained and evaluated on these capabilities: