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**An experiment exploring the feasibility of small language models for general-purpose coding and high-quality 3D scene generation.** This is an early preview of a 4B model I am still training, with a particular focus on creating interactive 3D scenes with Three.js. Initial hands-on results have been promising for coding in general, and especially for generating 3D scenes, so I decided to share the progress while development continues. This is an **independent fine-tune** by CCORE, based on Qwen/Qwen3.5-4B. **It is not an official Qwen release.**

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> **HuggingFace's "Hardware Compatibility" widget doesn't recognize K_P quants** — it may show fewer files than actually exist. Click **"View +X variants"** or go to **Files and versions** to see all available downloads. No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals. Stronger uncensoring — model is fully unlocked and won't refuse prompts. May occasionally append short disclaimers (baked into base model training, not refusals) but full content is always generated.

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I built this quant because the ready-made FP4 file answered the wrong question. It was fast, but on my short WikiText-2 control it scored 6.4949 PPL. Plain Q4_0 scored 6.3798. The first higher-quality hybrid went too far the other way: good perplexity, 34.19 tok/s, and no comfortable room for 256K plus vision. It is a 17.1 GB, 5.01 BPW mixed-precision GGUF of Qwen/Qwen3.8-27B. It keeps large, tolerant matrices in native NVFP4 and spends more bits on selected attention, Gated DeltaNet, and late FFN tensors. The trained MTP layer remains embedded in the same GGUF. This is not a fine-tune. I built the private calibration workload from 5,472 messages across 296 real sessions in my Hermes agent setup. The 153,600 processed tokens covered coding, infrastructure work, tool calls, and mixed Polish-English conversation. I used `llama-imatrix` to rank tensor sensitivity; the corpus and raw importance matrix are not distributed or embedded in the model.

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文本生成开放权重
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We consider the main ESG categories commonly addressed in ESG reports. These categories represent the primary thematic areas under which a company's performance and practices are assessed. They provide a structured framework for evaluating and reporting on various aspects of a company's impact on the environment, society, and its governance practices. The ESG classifier's labeling system embodies a nuanced hierarchy, delineating insights across three levels of granularity. At its foundational tier, it articulates broad categories encapsulating Environmental, Social, and Governance (ESG) considerations. Branching further, the second tier branches into 16 distinct classes, adding depth to the understanding of each ESG dimension. Finally, the third tier dives into meticulous detail, with 119 finely delineated classes. - **Carbon Footprint**: environment_climate_change_mitigation_carbon_footprint - **Circular Economy**: environment_climate_change_mitigation_circular_economy - **Circular Supply Chains**: environment_climate_change_mitigation_circular_supply_chains - **Energy Consumption Intensity**: environment_climate_change_mitigation_energy_consumption_intensity - **Greenhouse Gas Emissions**: environment_climate_change_mitigation_greenhouse_gas_emissions - **Heatwave Mitigation**: environment_climate_change_mitigation_heatwave_mitigation - **Product and Service Innovation**: environment_climate_change_mitigation_product_and_service_innovation - **Renewable Energy Usage**: environment_climate_change_mitigation_renewable_energy_usage - **Supply Chain Impact**: environment_climate_change_mitigation_supply_chain_impact - **Use and Production of Fossil Fuels**: environment_climate_change_mitigation_use_and_production_of_fossil_fuels - **Water Consumption Intensity**: environment_climate_change_mitigation_water_consumption_intensity - **Advocacy and Policy Engagement**: environment_climate_change_adaptation_advocacy_and_policy_engagement - **Climate Risk Management**: environment_climate_change_adaptation_climate_risk_management - **Coastal Erosion Prevention**: environment_climate_change_adaptation_coastal_erosion_prevention - **Drought Preparedness**: environment_climate_change_adaptation_drought_preparedness - **Flood Defenses**: environment_climate_change_adaptation_flood_defenses - **Infrastructure Adaptation**: environment_climate_change_adaptation_infrastruc

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

图像生成
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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

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。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

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。

开放权重，就代表可以免费商用吗？+

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。

排行榜第一，就是最适合我的模型吗？+

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。

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