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MAmmoTH2-7B-Plus
TIGER-LabIntroducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 36.7% on MATH and from 36% to 68.4% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities. The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details. The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
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View details ↗MAmmoTH2-8B-Plus
TIGER-LabIntroducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 36.7% on MATH and from 36% to 68.4% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities. | | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** | |:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------| | 7B | Mistral | 🦣 MAmmoTH2-7B | 🦣 MAmmoTH2-7B-Plus | | 8B | Llama-3 | 🦣 MAmmoTH2-8B | 🦣 MAmmoTH2-8B-Plus | | 8x7B | Mixtral | 🦣 MAmmoTH2-8x7B | 🦣 MAmmoTH2-8x7B-Plus | Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details. The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details. The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
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View details ↗MAmmoTH2-8x7B-Plus
TIGER-LabIntroducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 36.7% on MATH and from 36% to 68.4% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities. The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details. The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
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View details ↗Mantis-8B-siglip-llama3
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View details ↗VideoScore-v1.1
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View details ↗VideoScore2
TIGER-Lab📃Paper | 🌐Website | 💻Code | 🛢️Dataset (VideoFeedback2) | 🤗Model (VideoScore2) | 🤗Space (VideoScore2) We present VideoScore2, a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality, text-to-video alignment, and physical/common-sense consistency while producing detailed chain-of-thought rationales. Our model is trained on a large-scale dataset VideoFeedback2 containing 27,168 human-annotated videos with both scores and reasoning traces across three dimensions, using a two-stage pipeline of supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO) to enhance analytical robustness.
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View details ↗VL-Rethinker-7B
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View details ↗VLM2Vec-Full
TIGER-LabThis repo contains the code and data for VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks. In this paper, we aimed at building a unified multimodal embedding model for any tasks. Our model is based on converting an existing well-trained VLM (Phi-3.5-V) into an embedding model. **We’ve released several VLM2Vec models built on different VLM backbones: https://huggingface.co/collections/TIGER-Lab/vlm2vec-6705f418271d085836e0cdd5** **Also, the performance of these models is updated in the README of our GitHub repository: https://github.com/TIGER-AI-Lab/VLM2Vec/blob/main/README.md**
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View details ↗VLM2Vec-LoRA
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