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This model was converted to GGUF format from [`deepcogito/cogito-v1-preview-llama-8B`](https://huggingface.co/deepcogito/cogito-v1-preview-llama-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/deepcogito/cogito-v1-preview-llama-8B) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`deepcogito/cogito-v1-preview-llama-8B`](https://huggingface.co/deepcogito/cogito-v1-preview-llama-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/deepcogito/cogito-v1-preview-llama-8B) for more details on the model.
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---
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The Cogito LLMs are instruction tuned generative models (text in/text
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out). All models are released under an open license for commercial use.
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-Cogito models are hybrid reasoning models. Each model can answer
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directly (standard LLM), or self-reflect before answering (like
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reasoning models).
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-The LLMs are trained using Iterated Distillation and Amplification (IDA) - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement.
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-The models have been optimized for coding, STEM, instruction
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following and general helpfulness, and have significantly higher
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multilingual, coding and tool calling capabilities than size equivalent
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counterparts.
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--In both standard and reasoning modes, Cogito v1-preview models
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outperform their size equivalent counterparts on common industry
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benchmarks.
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Each model is trained in over 30 languages and supports a context length of 128k.
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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