Triangle104/Arcee-Blitz-Q3_K_M-GGUF

This model was converted to GGUF format from arcee-ai/Arcee-Blitz 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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Arcee-Blitz (24B) is a new Mistral-based 24B model distilled from DeepSeek, designed to be both fast and efficient. We view it as a practical “workhorse” model that can tackle a range of tasks without the overhead of larger architectures.

Model Details

Architecture Base: Mistral-Small-24B-Instruct-2501 Parameter Count: 24B Distillation Data: Merged Virtuoso pipeline with Mistral architecture, hotstarting the training with over 3B tokens of pretraining distillation from DeepSeek-V3 logits

Fine-Tuning and Post-Training: After capturing core logits, we performed additional fine-tuning and distillation steps to enhance overall performance.

License: Apache-2.0

Improving World Knowledge

Arcee-Blitz shows large improvements to performance on MMLU-Pro versus the original Mistral-Small-3, reflecting a dramatic increase in world knowledge.

Data contamination checking

We carefully examined our training data and pipeline to avoid contamination. While we’re confident in the validity of these gains, we remain open to further community validation and testing (one of the key reasons we release these models as open-source).

Limitations

Context Length: 32k Tokens (may vary depending on the final tokenizer settings and system resources). Knowledge Cut-off: Training data may not reflect the latest events or developments beyond June 2024.

Ethical Considerations

Content Generation Risks: Like any language model, Arcee-Blitz can generate potentially harmful or biased content if prompted in certain ways.

License

Arcee-Blitz (24B) is released under the Apache-2.0 License. You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.

If you have questions or would like to share your experiences using Arcee-Blitz (24B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!

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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/Arcee-Blitz-Q3_K_M-GGUF --hf-file arcee-blitz-q3_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Arcee-Blitz-Q3_K_M-GGUF --hf-file arcee-blitz-q3_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/Arcee-Blitz-Q3_K_M-GGUF --hf-file arcee-blitz-q3_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Arcee-Blitz-Q3_K_M-GGUF --hf-file arcee-blitz-q3_k_m.gguf -c 2048