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  This model was converted to GGUF format from [`anthracite-org/magnum-v4-22b`](https://huggingface.co/anthracite-org/magnum-v4-22b) 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/anthracite-org/magnum-v4-22b) 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 [`anthracite-org/magnum-v4-22b`](https://huggingface.co/anthracite-org/magnum-v4-22b) 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/anthracite-org/magnum-v4-22b) for more details on the model.
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+ ---
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+ Model details:
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+ -
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+ This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.
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+
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+ This model is fine-tuned on top of Mistral-Small-Instruct-2409.
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+
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+ Prompting
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+ -
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+ A typical input would look like this:
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+
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+ <s>[INST] SYSTEM MESSAGE
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+ USER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]
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+
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+ Credits
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+ -
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+ We'd like to thank Recursal / Featherless for sponsoring the compute for this train, Featherless has been hosting our Magnum models since the first 72 B and has given thousands of people access to our models and helped us grow.
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+
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+ We would also like to thank all members of Anthracite who made this finetune possible.
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+
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+ Datasets
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+ -
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+ anthracite-org/c2_logs_32k_mistral-v3_v1.2_no_system
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+ anthracite-org/kalo-opus-instruct-22k-no-refusal-no-system
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+ anthracite-org/kalo-opus-instruct-3k-filtered-no-system
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+ anthracite-org/nopm_claude_writing_fixed
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+ anthracite-org/kalo_opus_misc_240827_no_system
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+ anthracite-org/kalo_misc_part2_no_system
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+
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+ Training
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+ -
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+ The training was done for 2 epochs. We used 8xH100s GPUs graciously provided by Recursal AI / Featherless AI for the full-parameter fine-tuning of the model.
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+
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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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