darkc0de

Xortron2025, Uncensored Large Language Model for Offline and Local use.

You can try this model now for free at xortron.tech This model will help you do anything and everything you probably shouldn't be doing. As of this writing (July 2025), this model tops the UGI Leaderboard for models under 70 billion parameters in both the UGI and W10 categories.

darkc0de/XortronCriminalComputingConfig-Q5KS-GGUF This model was converted to GGUF format from `darkc0de/XortronCriminalComputingConfig` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) 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 `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

darkc0de/BlackXorDolphTronGOAT-Q5KS-GGUF This model was converted to GGUF format from `darkc0de/BlackXorDolphTronGOAT` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) 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 `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

darkc0de/Mistral-Small-24B-Instruct-2501-abliterated-Q5KM-GGUF This model was converted to GGUF format from `huihui-ai/Mistral-Small-24B-Instruct-2501-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. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) 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 `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

darkc0de/SmolLM2-1.7B-Instruct-Q80-GGUF This model was converted to GGUF format from `HuggingFaceTB/SmolLM2-1.7B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) 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 `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

darkc0de/BuddyGlassUncensored2025.2-Q5KM-GGUF This model was converted to GGUF format from `darkc0de/BuddyGlassUncensored2025.2` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) 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 `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

This is a merge of pre-trained language models created using mergekit. This model was merged using the TIES merge method using tiiuae/Falcon3-10B-Base as a base. The following models were included in the merge: huihui-ai/Falcon3-10B-Instruct-abliterated The following YAML configuration was used to produce this model:

This is a merge of pre-trained language models created using mergekit. This model was merged using the DARE TIES merge method using mistralai/Mistral-Small-24B-Instruct-2501 as a base. The following models were included in the merge: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated TheDrummer/Cydonia-24B-v2 huihui-ai/Arcee-Blitz-abliterated cognitivecomputations/Dolphin3.0-Mistral-24B The following YAML configuration was used to produce this model: