Model overview
`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
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。