relweave-4b-base
chrullis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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fare2-clip
chs20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 ↗fare4-clip
chs20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 ↗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 ↗tecoa2-clip
chs20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 ↗tecoa4-clip
chs20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 ↗> **On NVIDIA GPUs, use llama.cpp b11390 (4 October 2026) or newer.** Older > CUDA builds crash on prompts of certain lengths (230, 344 and 457 tokens > in our tests) with "CUDA error: an illegal memory access was encountered". > Other lengths work normally, and CPU-only builds are not affected. > Upstream fix: #29941. This is a GGUF conversion of `ISTA-DASLab/Qwen3.6-35B-A3B-2Bit-GSQ`. It is essentially the DASLab GSQ checkpoint repackaged into stock llama.cpp types, with the small conversion errors documented below. It is not a new quantization, fine-tune, or model release. It is distributed as a text-model GGUF plus a separate BF16/F32 vision-projector GGUF. The routed experts use Q2_0 (source: 2-bit GSQ), while attention, linear attention, shared-expert, and LM-head weights use Q8_0 (source: INT8). Remaining text-model tensors use BF16 or F32 (source: BF16) as required by llama.cpp's canonical `QWEN35MOE` layout. The companion projector retains the source BF16 vision tower as BF16 or exact F32 promotions. MTP weights remain omitted.
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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 ↗chua-dl-models
chtk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 ↗Llama-3.2-3B-Instruct-uncensored
chuanli11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 ↗ML-Agents-SoccerTwos-New
chubbyk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 ↗BiomedCLIP-vit-bert-hf
chuhac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 ↗gemma-4-E4B-it-uncensored-vision
ChuHo1994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 ↗internlm2-chat-20b-ExPO
chujiezhengThe extrapolated (ExPO) model based on `internlm2-chat-20b` and `internlm/internlm2-chat-20b-sft`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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View details ↗internlm2-chat-7b-ExPO
chujiezhengThe extrapolated (ExPO) model based on `internlm2-chat-7b` and `internlm/internlm2-chat-7b-sft`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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View details ↗Llama3-70B-Chinese-Chat-ExPO
chujiezhengThe extrapolated (ExPO) model based on `shenzhi-wang/Llama3-70B-Chinese-Chat` and `meta-llama/Meta-Llama-3-70B-Instruct`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. **Note:** This is an experimental model, as I have not comprehensively evaluated its Chinese ability. **Unexpected issues may occur when we apply extrapolation to the DPO/RLHF alignment training for new languages (e.g., Chinese).**
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View details ↗Llama3-8B-Chinese-Chat-ExPO
chujiezhengThe extrapolated (ExPO) model based on `shenzhi-wang/Llama3-8B-Chinese-Chat` and `meta-llama/Meta-Llama-3-8B-Instruct`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. **Note:** This is an experimental model, as I have not comprehensively evaluated its Chinese ability. **Unexpected issues may occur when we apply extrapolation to the DPO/RLHF alignment training for new languages (e.g., Chinese).**
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View details ↗LLaMA3-iterative-DPO-final-ExPO
chujiezhengThe extrapolated (ExPO) model based on `RLHFlow/LLaMA3-iterative-DPO-final` and `RLHFlow/LLaMA3-SFT`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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View details ↗Mistral7B-PairRM-SPPO-ExPO
chujiezhengThe extrapolated (ExPO) model based on `UCLA-AGI/Mistral7B-PairRM-SPPO` and `mistralai/Mistral-7B-Instruct-v0.2`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. This extrapolated model achieves the **35.4%** win rate and **31.8%** LC win rate on **AlpacaEval 2.0**, outperforming the original `Mistral7B-PairRM-SPPO`'s 32.2% and 30.5%, respectively.
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View details ↗Smaug-34B-v0.1-ExPO
chujiezhengThe extrapolated (ExPO) model based on `abacusai/Smaug-34B-v0.1` and `jondurbin/bagel-34b-v0.2`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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View details ↗Smaug-Llama-3-70B-Instruct-ExPO
chujiezhengThe extrapolated (ExPO) model based on `abacusai/Smaug-Llama-3-70B-Instruct` and `meta-llama/Meta-Llama-3-70B-Instruct`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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View details ↗Starling-LM-7B-alpha-ExPO
chujiezhengThe extrapolated (ExPO) model based on `berkeley-nest/Starling-LM-7B-alpha` and `openchat/openchat_3.5`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.2)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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View details ↗Starling-LM-7B-beta-ExPO
chujiezhengThe extrapolated (ExPO) model based on `Nexusflow/Starling-LM-7B-beta` and `openchat/openchat-3.5-0106`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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