Triangle104/magnum-v4-22b-Q5_K_M-GGUF

This model was converted to GGUF format from anthracite-org/magnum-v4-22b using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.

This model is fine-tuned on top of Mistral-Small-Instruct-2409.

Prompting

A typical input would look like this:

[INST] SYSTEM MESSAGE USER MESSAGE[/INST] ASSISTANT MESSAGE[INST] USER MESSAGE[/INST]

Credits

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.

We would also like to thank all members of Anthracite who made this finetune possible.

Datasets

anthracite-org/c2_logs_32k_mistral-v3_v1.2_no_system
anthracite-org/kalo-opus-instruct-22k-no-refusal-no-system
anthracite-org/kalo-opus-instruct-3k-filtered-no-system
anthracite-org/nopm_claude_writing_fixed
anthracite-org/kalo_opus_misc_240827_no_system
anthracite-org/kalo_misc_part2_no_system

Training

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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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/magnum-v4-22b-Q5_K_M-GGUF --hf-file magnum-v4-22b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/magnum-v4-22b-Q5_K_M-GGUF --hf-file magnum-v4-22b-q5_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

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).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/magnum-v4-22b-Q5_K_M-GGUF --hf-file magnum-v4-22b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/magnum-v4-22b-Q5_K_M-GGUF --hf-file magnum-v4-22b-q5_k_m.gguf -c 2048