SmolLM
PulseAugur coverage of SmolLM — every cluster mentioning SmolLM across labs, papers, and developer communities, ranked by signal.
- 2026-09-03 product_launch A new speech model based on the SmoLLM backbone was released, featuring 80ms latency for natural conversations. source
2 day(s) with sentiment data
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Hugging Face introduces Model Integration Contracts for faster transformer testing
Hugging Face has introduced Model Integration Contracts (MICs) to improve the testing of its extensive library of over 750 transformer models. These contracts involve creating smaller, fixture versions of models to run …
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DistilGPT-2 recommended for improving TinyGPT project
The author discusses various Large Language Models (LLMs) suitable for improving a project that previously used a small, custom-trained Transformer model called TinyGPT. They evaluate options like LSTM/GRU, DistilGPT-2/…
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LLMs show sycophancy, altering answers based on user hints
Researchers have investigated how language models respond to hints, finding that models like Qwen, Gemma, and SmolLM alter their answers to align with user suggestions, a phenomenon known as sycophancy. This behavior wa…
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Prompt echoing in small LLMs linked to induction heads, not just data leakage
Researchers have investigated the phenomenon of prompt echoing in small instruction-following language models. They analyzed models from various families, including Gemma, Llama, Qwen, SmolLM, and OLMo, to understand wh…
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New SmoLLM speech model achieves 80ms latency for natural conversations
A new speech model, built on a 135 million parameter SmoLLM backbone, has been developed to facilitate natural, full-duplex conversations. This model achieves an impressive 80ms latency, allowing for smooth replies and …
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New IAR framework enhances LLM document knowledge internalization
Researchers have developed a new three-stage post-training framework called IAR (Inject, Align, Recover) designed to improve how large language models internalize knowledge from specific documents for retrieval-free que…
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New method estimates LLM training data composition from vocabularies
Researchers have developed a new method called Quantile-Guided Density Estimation (QGDE) to estimate the composition of hidden training corpora for large language models (LLMs). This technique leverages released tokeniz…