Triangle104/Mistral-Small-Drummer-22B-Q8_0-GGUF

This model was converted to GGUF format from nbeerbower/Mistral-Small-Drummer-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:

mistralai/Mistral-Small-Instruct-2409 finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.

Method ORPO tuned with 2xA40 on RunPod for 1 epoch.

learning_rate=4e-6, lr_scheduler_type="linear", beta=0.1, per_device_train_batch_size=4, per_device_eval_batch_size=4, gradient_accumulation_steps=8, optim="paged_adamw_8bit", num_train_epochs=1,

Dataset was prepared using Mistral-Small Instruct format.

Fine-tune Llama 3 with ORPO

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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/Mistral-Small-Drummer-22B-Q8_0-GGUF --hf-file mistral-small-drummer-22b-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Mistral-Small-Drummer-22B-Q8_0-GGUF --hf-file mistral-small-drummer-22b-q8_0.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/Mistral-Small-Drummer-22B-Q8_0-GGUF --hf-file mistral-small-drummer-22b-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Mistral-Small-Drummer-22B-Q8_0-GGUF --hf-file mistral-small-drummer-22b-q8_0.gguf -c 2048