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Model · Text generation

OLMo-2-0425-1B

Ai2

We introduce OLMo 2 1B, the smallest model in the OLMo 2 family. OLMo 2 was pre-trained on OLMo-mix-1124 and uses Dolmino-mix-1124 for mid-training. OLMo 2 is the latest in a series of Open Language Models designed to enable the science of language models. We have released all code, checkpoints, logs, and associated training details on GitHub. The core models released in this batch include the following: OLMo 2 1B is supported in transformers v4.48 or higher: If using vLLM, you will need to install from the main branch until v0.7.4 is released. Please You can use OLMo with the standard HuggingFace transformers library: For faster performance, you can quantize the model using the following…

Model · Text generation

AMOR-GatedDeltaNet-1.5B

Haoran Zheng

Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. For a smaller download, see the 180M and 440M models below. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and…

Model · Text generation

AMOR-Mamba2-1.5B

Haoran Zheng

Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. For a smaller download, see the 180M and 440M models below. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and…

Model · Text generation

Qwen2.5-1.5B-Instruct

Qwen

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and…

Model · Text generation

Qwen2.5-1.5B

Qwen

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and…

Model · Text generation

logi-1.5b

Alves

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.