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--- |
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license: mit |
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library_name: transformers |
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datasets: |
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- AI-MO/NuminaMath-CoT |
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- KbsdJames/Omni-MATH |
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- RUC-AIBOX/STILL-3-Preview-RL-Data |
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- hendrycks/competition_math |
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language: |
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- en |
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base_model: agentica-org/DeepScaleR-1.5B-Preview |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/DeepScaleR-1.5B-Preview-Q5_K_S-GGUF |
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This model was converted to GGUF format from [`agentica-org/DeepScaleR-1.5B-Preview`](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview) 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/agentica-org/DeepScaleR-1.5B-Preview) for more details on the model. |
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--- |
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DeepScaleR-1.5B-Preview is a language model fine-tuned from |
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DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning |
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(RL) to scale up to long context lengths. The model achieves 43.1% |
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Pass@1 accuracy on AIME 2024, representing a 15% improvement over the |
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base model (28.8%) and surpassing OpenAI's O1-Preview performance with |
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just 1.5B parameters. |
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Data |
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Our training dataset consists of approximately 40,000 unique problem-answer pairs compiled from: |
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AIME problems (1984-2023) |
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AMC problems (prior to 2023) |
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Omni-MATH dataset |
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Still dataset |
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Training Recipe |
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We employ Deepseek's Group Relative Policy Optimization (GRPO), a simplified RL algorithm that extends PPO by: |
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Normalizing advantage function over all samples generated from the same prompt. |
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Applying KL divergence regularization on top of PPO's surrogate loss to prevent significant policy drift. |
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Reward Function: Our reward function is simple but effective: |
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1 for correct answers passing LaTeX/Sympy checks |
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0 for incorrect or improperly formatted answers |
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Note: No partial rewards (such as PRMs) or intermediate feedback. |
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Iterative Context Lengthening: A key challenge in |
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scaling RL for reasoning is compute cost. Our approach trains models |
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with progressively longer contexts as the model improves, thus saving |
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monetary costs and end2end training time: |
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Initial 8K Context (0-1040 steps): |
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22.9% -> 33% Pass@1 on AIME 2024 |
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Trained on 8 A100-80GB GPUs, BS= (Prompts) * (Samples/Prompt) = 128 * 8 = 1024 |
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Extended to 16K (steps 1040-1520): |
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33% -> 43% Pass@1 on AIME 2024 |
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Trained on 32 A100-80GB GPUs, BS= (Prompts) * (Samples/Prompt) = 128 * 16 = 2048 |
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Further extended to 24K (step 1520+): |
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38% -> 43% Pass@1 on AIME 2024 |
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Trained on 32 A100-80GB GPUs, BS= (Prompts) * (Samples/Prompt) = 128 * 16 = 2048 |
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Significant improvements within <200 steps |
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A more detailed description of the training recipe can be found in our blog post. |
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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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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/DeepScaleR-1.5B-Preview-Q5_K_S-GGUF --hf-file deepscaler-1.5b-preview-q5_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/DeepScaleR-1.5B-Preview-Q5_K_S-GGUF --hf-file deepscaler-1.5b-preview-q5_k_s.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/DeepScaleR-1.5B-Preview-Q5_K_S-GGUF --hf-file deepscaler-1.5b-preview-q5_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/DeepScaleR-1.5B-Preview-Q5_K_S-GGUF --hf-file deepscaler-1.5b-preview-q5_k_s.gguf -c 2048 |
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``` |
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