Triangle104/DeepScaleR-1.5B-Preview-Q5_K_M-GGUF

This model was converted to GGUF format from agentica-org/DeepScaleR-1.5B-Preview 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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DeepScaleR-1.5B-Preview is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 43.1% Pass@1 accuracy on AIME 2024, representing a 15% improvement over the base model (28.8%) and surpassing OpenAI's O1-Preview performance with just 1.5B parameters.

    Data

Our training dataset consists of approximately 40,000 unique problem-answer pairs compiled from:

AIME problems (1984-2023) AMC problems (prior to 2023) Omni-MATH dataset Still dataset

    Training Recipe

We employ Deepseek's Group Relative Policy Optimization (GRPO), a simplified RL algorithm that extends PPO by:

Normalizing advantage function over all samples generated from the same prompt. Applying KL divergence regularization on top of PPO's surrogate loss to prevent significant policy drift.

Reward Function: Our reward function is simple but effective:

1 for correct answers passing LaTeX/Sympy checks 0 for incorrect or improperly formatted answers Note: No partial rewards (such as PRMs) or intermediate feedback.

Iterative Context Lengthening: A key challenge in scaling RL for reasoning is compute cost. Our approach trains models with progressively longer contexts as the model improves, thus saving monetary costs and end2end training time:

Initial 8K Context (0-1040 steps): 22.9% -> 33% Pass@1 on AIME 2024 Trained on 8 A100-80GB GPUs, BS= (Prompts) * (Samples/Prompt) = 128 * 8 = 1024

Extended to 16K (steps 1040-1520): 33% -> 43% Pass@1 on AIME 2024 Trained on 32 A100-80GB GPUs, BS= (Prompts) * (Samples/Prompt) = 128 * 16 = 2048

Further extended to 24K (step 1520+): 38% -> 43% Pass@1 on AIME 2024 Trained on 32 A100-80GB GPUs, BS= (Prompts) * (Samples/Prompt) = 128 * 16 = 2048 Significant improvements within <200 steps

A more detailed description of the training recipe can be found in our blog post.

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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/DeepScaleR-1.5B-Preview-Q5_K_M-GGUF --hf-file deepscaler-1.5b-preview-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/DeepScaleR-1.5B-Preview-Q5_K_M-GGUF --hf-file deepscaler-1.5b-preview-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/DeepScaleR-1.5B-Preview-Q5_K_M-GGUF --hf-file deepscaler-1.5b-preview-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/DeepScaleR-1.5B-Preview-Q5_K_M-GGUF --hf-file deepscaler-1.5b-preview-q5_k_m.gguf -c 2048