从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。
This is an uncensored version of Qwen/Qwen3-Coder-30B-A3B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Ablation was performed using a new and faster method, which yields better results. You can use huihui_ai/qwen3-coder-abliterated directly,
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This is an uncensored version of Qwen/Qwen3-VL-4B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). **Please update to the latest version of Ollama-v0.12.7.** You can use huihui_ai/qwen3-vl-abliterated:4b-instruct directly,
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This is an uncensored version of Qwen/Qwen3-VL-8B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). The abliterated model will no longer say "I can’t describe or analyze this image." **Please update to the latest version of Ollama-v0.12.7.** You can use huihui_ai/qwen3-vl-abliterated:8b-instruct directly,
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This is an uncensored version of empero-ai/Qwythos-9B-Claude-Mythos-5-1M created with abliteration. **Note** In non-thinking mode, this model strictly adheres to the first prompt's instructions, delivering only the requested output—whether a translation or a specific format—without any introductory or concluding fluff. Please use the latest version of ggml-org/llama.cpp
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`Huihui4-8B-A4B` is a lightweight MoE (Mixture of Experts) conversational model optimized from Google's `gemma-4-26B-A4B-it` architecture. Through expert pruning and supervised fine-tuning on high-quality dialogue data, this model significantly reduces computational overhead while preserving core reasoning and interaction capabilities. It is specifically designed for deployment on consumer-grade hardware and code-related conversational tasks. 1. **Multi-Domain Fine-Tuning:** Further SFT on **four distinct datasets** to enhance the generalization capabilities of this 32-expert model. 2. **Expert Merging Validation:** Experiment with merging the four independently fine-tuned models back into a **128-expert architecture**, validating the feasibility of a `"prune → fine-tune → merge"` pipeline. 3. **Core Objective:** Ultimately verify the engineering viability of **training and iterating on large-scale MoE models using only consumer-grade hardware**. 4. If you're interested, feel free to fine-tune this model on your own datasets. We plan to merge all resulting models into a unified version at the end. model | Fine-tuning steps | num_experts | Perplexity | Average Loss ----------------------------------------|--------------------|-------------|-------------------|------------------- gemma-4-26B-A4B-it | 0 | 128 | 1.5964 (+ 0 ) | 0.4678 (+ 0 ) gemma-4-26B-A4B-it-Pruned-32 | 0 | 32 | 2.4826 (+ 0.8862) | 0.9093 (+ 0.4415) gemma-4-26B-A4B-it-Pruned-32-sft-750 | 750 | 32 | 1.3827 (- 0.2137) | 0.3240 (- 0.1438) gemma-4-26B-A4B-it-Pruned-32-sft-1350 | 1350 | 32 | 1.2374 (- 0.359 ) | 0.2130 (- 0.2548) gemma-4-26B-A4B-it-Pruned-32-sft-1800 | 1800 | 32 | 1.1724 (- 0.424 ) | 0.1590 (- 0.3088) gemma-4-26B-A4B-it-Pruned-32-sft-2950 | 2950 | 32 | 1.0924 (- 0.504 ) | 0.0883 (- 0.3795) gemma-4-26B-A4B-it-Pruned-32-sft-3550 | 2950 | 32 | 1.0645 (- 0.5319) | 0.0625 (- 0.4053) gemma-4-26B-A4B-it-Pruned-32-sft-4150 | 4150 | 32 | 1.0532 (- 0.5432) | 0.0518 (- 0.416 ) gemma-4-26B-A4B-it-Pruned-32-sft-4700 | 4700 | 32 | 1.0411 (- 0.5553) | 0.0403 (- 0.4275) gemma-4-26B-A4B-it-Pruned-32-sft-7800 | 7800 | 32 | 1.0088 (- 0.5876) | 0.0088 (- 0.459 ) gemma-4-26B-A4B-it-Pruned-32-sft-10900 | 109
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This is an uncensored version of meta-llama/Llama-3.3-70B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. You can use huihui_ai/llama3.3-abliterated directly, Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
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This is an uncensored version of Qwen/Qwen2.5-7B-Instruct created with abliteration (see this article to know more about it). Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models. **Important Note** This version is an improvement over the previous one Qwen2.5-7B-Instruct-abliterated.
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--- license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - abliterated - uncensored library_name: transformers base_model: - Qwen/Qwen2.5-VL-3B-Instruct --- This is an uncensored version of Qwen/Qwen2.5-VL-3B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). You can use huihui_ai/qwen2.5-vl-abliterated:3b directly,
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--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal - abliterated - uncensored library_name: transformers base_model: - Qwen/Qwen2.5-VL-7B-Instruct --- This is an uncensored version of Qwen/Qwen2.5-VL-7B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). You can use huihui_ai/qwen2.5-vl-abliterated:7b directly,
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
