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View details ↗DISCOVER & COMPARE
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Qwen3-Coder-30B-A3B-Instruct-AWQ
AIDXteam**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: **Qwen3-Coder-30B-A3B-Instruct** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**. **NOTE: This model supports only non-thinking mode and does not generate `` `` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
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View details ↗MathBERT-mamut
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View details ↗feifei-flux-lora-v1
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View details ↗flux-lora-uncensored
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View details ↗Gemma-4-Queen-31B-it
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View details ↗Qwen3-0.6B-D-ROS
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View details ↗Qwen3.5-Queen-27B
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View details ↗FLUX.1-schnell-Quantized
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View details ↗OLMo-1B-0724-hf-Quantized
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View details ↗OLMo-7B-0424-hf-Quantized
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View details ↗OLMo-7B-0724-Instruct-hf-Quantized
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View details ↗BioGravity-bilingual-Inst
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View details ↗BioGravity-Inst
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View details ↗RudolfV-2
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View details ↗RudolfV-2-B
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View details ↗Qwen3-14B-abliterated-Q4_K_S-GGUF
AIgotaholeThis model was converted to GGUF format from `evolveon/Qwen3-14B-abliterated` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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View details ↗LlamaHarmony
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View details ↗hash-MedGemma-4B-16bit-eng-swa-it
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View details ↗Sunflower-Qwen-9B-medical-16bit
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View details ↗Kiriku-Wolof-ASR
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