gemma-4-E4B-it-heretic
coder3101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。
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View details ↗DISCOVER & COMPARE
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View details ↗Qwen3.5-0.8B-heretic
coder3101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。
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View details ↗Qwen3.5-27B-heretic
coder3101> [!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. - **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.
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View details ↗Qwen3.5-4B-heretic
coder3101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。
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View details ↗Qwen3-VL-32B-Instruct-heretic-v2
coder3101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。
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View details ↗GLM-5.3-GGUF
Coder40-95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。
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View details ↗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。
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View details ↗anime-girl-1.5B
coderian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。
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View details ↗moshi-common
Codes4Fun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。
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View details ↗moshika-q4_k-GGUF
Codes4Fun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。
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View details ↗personaplex-7b-v1-q4_k-GGUF
Codes4Fun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。
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View details ↗codesage-base-v2
codesage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。
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View details ↗codesage-large
codesage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。
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View details ↗codesage-large-v2
codesageCodeSage is a family of open code embedding models with an encoder architecture that supports a wide range of source code understanding tasks. It was initially introduced in the paper: For this V2 model, we enhanced semantic search performance by improving the quality of the contrastive learning data through consistency filtering. Starting from the pretrained checkpoint (trained with both Masked Language Modeling (MLM) and deobfuscation Section 3.1) from our V1 model training (Zhang et al., 2023), we applied contrastive learning with the filtered data. Unlike the V1 model, we extracted the initial set of (text, code) pairs—specifically, summaries and function/class bodies—from The Stack V2 data instead of using the V1 data. We employed simple rule-based filtering as detailed in our previous work. We then applied consistency filtering to further refine the data. While using The Stack V2 resulted in minor performance boosts on downstream tasks, the majority of the performance improvements came from the consistency filtering. This pretrained checkpoint is the same as those used by our V1 model (codesage/codesage-small, which is trained on The Stack data. The constative learning data are extracted from The Stack V2. Same as our V1 model, we supported nine languages as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.
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View details ↗codesage-small
codesage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。
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View details ↗codesage-small-v2
codesage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。
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View details ↗Qwen3.5-9B-Instruct
CodeShield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。
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View details ↗sorbet-25m
CodeSoft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。
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View details ↗sorbet-v2-25m
CodeSoft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。
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View details ↗Llama3.2-3B-Claude-Reasoning-Distill
codestrate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。
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View details ↗vit-chest-xray
codewithdark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。
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View details ↗4f5bdb62
codexivory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。
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