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From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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A 124M model with the GPT-2 architecture trained with the next token prediction loss for 10 epochs (~900 M words) **on 90% of the BabyLM corpus** and an additional **1 M words of PPO RL** training as submission for the Interaction track of the 2025 BabyLM challenge. This is the RL storytelling model, based on a pretrained GPT-2 model, for the Interaction Track of the 2025 BabyLM challenge. This is a pre-trained language model. It can be used to evaluate tasks in a zero-shot manner and also can be fine-tuned for downstream tasks. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance.
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NVFP4-quantized version of `Qwen/Qwen3-4B-Instruct-2507` produced with llmcompressor. - Quantization scheme: NVFP4 (linear layers, `lm_head` excluded) - Calibration samples: 512 - Max sequence length during calibration: 2048 This model can be deployed efficiently using the vLLM backend, as shown in the example below.
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🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨 I can no longer upload new models unless I can cover the cost of additional storage. I host 70+ free models as an independent contributor and this work is unpaid. Without your support, no more new models can be uploaded. Creating these models takes significant time, work and compute. If you find them useful consider supporting me: Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
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🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨 I can no longer upload new models unless I can cover the cost of additional storage. I host 70+ free models as an independent contributor and this work is unpaid. Without your support, no more new models can be uploaded. Creating these models takes significant time, work and compute. If you find them useful consider supporting me: Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
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基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨 I can no longer upload new models unless I can cover the cost of additional storage. I host 70+ free models as an independent contributor and this work is unpaid. Without your support, no more new models can be uploaded. Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone. Creating these models takes significant time, work and compute. If you find them useful consider supporting me:
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🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨 I can no longer upload new models unless I can cover the cost of additional storage. I host 70+ free models as an independent contributor and this work is unpaid. Without your support, no more new models can be uploaded. Creating these models takes significant time, work and compute. If you find them useful consider supporting me: Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
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🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨 I can no longer upload new models unless I can cover the cost of additional storage. I host 70+ free models as an independent contributor and this work is unpaid. Without your support, no more new models can be uploaded. Creating these models takes significant time, work and compute. If you find them useful consider supporting me: Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.