led-large-16384
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led-large-16384-arxiv
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View details ↗Llama-3.1-8B-Instruct-RM-RB2
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View details ↗Llama-3.1-Tulu-3.1-8B
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View details ↗Llama-3.1-Tulu-3-405B
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View details ↗Llama-3.1-Tulu-3-405B-SFT
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View details ↗Llama-3.1-Tulu-3-70B
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View details ↗Llama-3.1-Tulu-3-70B-DPO
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View details ↗Llama-3.1-Tulu-3-70B-SFT
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View details ↗Llama-3.1-Tulu-3-8B
allenaiTülu 3 is a leading instruction following model family, offering a post-training package with fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern techniques. This is one step of a bigger process to training fully open-source models, like our OLMo models. Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. PPO settings for RLVR: - **Learning Rate**: 3 × 10⁻⁷ - **Discount Factor (gamma)**: 1.0 - **General Advantage Estimation (lambda)**: 0.95 - **Mini-batches (N_mb)**: 1 - **PPO Update Iterations (K)**: 4 - **PPO's Clipping Coefficient (epsilon)**: 0.2 - **Value Function Coefficient (c1)**: 0.1 - **Gradient Norm Threshold**: 1.0 - **Learning Rate Schedule**: Linear - **Generation Temperature**: 1.0 - **Batch Size (effective)**: 224 - **Max Token Length**: 2,048 - **Max Prompt Token Length**: 2,048 - **Penalty Reward Value for Responses without an EOS Token**: -10.0 - **Response Length**: 2,048 - **Total Episodes**: 100,000 - **KL penalty coefficient (beta)**: 0.05 - **Warm up ratio (omega)**: 0.0
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View details ↗Llama-3.1-Tulu-3-8B-DPO
allenaiTülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. DPO: - **Learning Rate**: 5 × 10⁻⁷ (8B), 2.0e-7 (70B, 405B) - **Learning Rate Schedule**: Linear - **Batch Size (effective)**: 128 (8B), 128 (70B), 256(405B) - **KL Penalty Coefficient**: 5 - **Warm-up Ratio**: 0.1 - **Max Sequence Length**: 2,048 - **Epochs**: 1
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View details ↗Llama-3.1-Tulu-3-8B-RM
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View details ↗Llama-3.1-Tulu-3-8B-SFT
allenaiTülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. SFT: - **Learning Rate**: 5E-6 (8B), 2E-6 (70B, 405B) - **Effective Batch Size:** 128 (8B, 70B), 256 (405B) - **Max. Sequence Length:** 4096 - **Loss Accumulation:** Sum (see https://unsloth.ai/blog/gradient) - **Learning Rate Schedule:** Linear - **LR Warmup Ratio:** 0.03 - **Num. Epochs:** 2
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View details ↗Llama-3-Blama-8B
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View details ↗Llama-3-Blama-8B-Stage1
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View details ↗llama-3-tulu-2-8b
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View details ↗llama-3-tulu-2-8b-uf-mean-rm
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View details ↗longformer-base-4096
allenai`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096. Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations. Please refer to the examples in `modeling_longformer.py` and the paper for more details on how to set global attention. If you use `Longformer` in your research, please cite Longformer: The Long-Document Transformer.
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View details ↗longformer-large-4096
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View details ↗longformer-scico
allenaiThis model is the `unified` model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass problem. The model takes as input two mentions `m1` and `m2` with their corresponding context and outputs 4 scores: * 0: not related * 1: `m1` and `m2` corefer * 2: `m1` is a parent of `m2` * 3: `m1` is a child of `m2`. We provide the following code as an example to set the global attention on the special tokens: ` `, ` ` and ` `.
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View details ↗Molmo-72B-0924
allenaiMolmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. You can find all models in the Molmo family here. **Learn more** about the Molmo family in our announcement blog post or the paper. Molmo 72B is based on Qwen2-72B and uses OpenAI CLIP as vision backbone. Molmo-72B achieves the highest academic benchmark score and ranks second on human evaluation, just slightly behind GPT-4o. This checkpoint is a **preview** of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
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View details ↗Molmo-7B-D-0924
allenaiMolmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. You can find all models in the Molmo family here. **Learn more** about the Molmo family in our announcement blog post or the paper. Molmo 7B-D is based on Qwen2-7B and uses OpenAI CLIP as vision backbone. It performs comfortably between GPT-4V and GPT-4o on both academic benchmarks and human evaluation. It powers the **Molmo demo at** **molmo.allenai.org**. This checkpoint is a **preview** of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
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View details ↗Molmo-7B-O-0924
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View details ↗Molmo2-4B
allenaiMolmo2 is a family of open vision-language models developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. Molmo2 models are trained on publicly available third party datasets as referenced in our technical report and Molmo2 data, a collection of datasets with highly-curated image-text and video-text pairs. It has state-of-the-art performance among multimodal models with a similar size. You can find all models in the Molmo2 family here. **Learn more** about the Molmo2 family in our announcement blog post. Molmo2-4B is based on Qwen3-4B-Instruct and uses SigLIP 2 as vision backbone. It outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos.
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