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NVIDIA Dynamo+60% downloads · 15.8k/wk+60.0%
BentoML+30% downloads · 41k/wk+29.7%
MLX+26% downloads · 342.6k/wk+25.5%
Open WebUI3 releases this week
mlx-lm+8% downloads · 140.9k/wk+7.5%

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Open problems in Small LLMs

Efficient quantization for small LLMs without accuracy loss3 papers raise it · 2 address it
Enables deployment of small models on resource-constrained devices while preserving performance.
- N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Modelsraises · addresses
- RoleQ: Role-Aligned Scales for Converting Language Models to Ternaryraises · addresses
- Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensorsraises
Secure on-device model deployment against reverse engineering1 paper raises it · 1 addresses it
Protects proprietary small models from IP theft and tampering in edge environments.
- BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partitionraises · addresses
Effective distillation for small LLMs with limited teacher access1 paper raises it · 1 addresses it
Improves small model performance when full teacher access or internal representations are unavailable.
- Recursive Offline Distillation via Entropy Gain Sampling and Answer Mixingraises · addresses
Task-aware tokenization for morphologically rich languages1 paper raises it · 1 addresses it
Improves model performance in languages with complex morphology by preserving linguistic structure.
- Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalograises · addresses
Dynamic generation order for sequence labeling in low-resource settings1 paper raises it · 1 addresses it
Reduces error propagation in autoregressive models for tasks with non-linear dependencies.
- Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenariosraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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InfinigenceSeries B+ · Oct 12$343.6M
Lean SuperIntelligencePre-seed · Oct 9undisclosed
VinciSeries B+ · Oct 6$250M
Siena AISeries A · Oct 6$17M
Ocular AIPre-seed · Oct 5$2M

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Active contributors · 90 days

Giacomo BaisDynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios1
Tejaswini DeoskarDynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios1
Noman KhanPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference1
Remus TeodorescuPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference1
Shahariya Rasheed Rayamarakkarveettil PuthanpurayilPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference1
Vlad Alexandru Sănduleac

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Activity · 12 weeks

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arXiv cs.LGOct 14

How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts

Annabelle Sujun Tang, Wei Tang, Songyao Jiang, Tianhui Li, Shengzhe Xu +5

Compiler transformations interact, making GPU kernel optimization a search for combinations that preserve the computation while improving performance. We introduce HOW TO TRAIN YOUR LITTLE DRAGON, a training method that teaches a small language model both to construct complete optimization scripts and to improve existing ones. Instead of ranking candidates or generating kernel code, our model generates optimization scripts. We devise a compact, lossless representation whose deterministic decoding reconstructs the exact compiler trace, preserving its transformation parameters and dependencies. We use a compiler framework TVM MetaSchedule to build training data on 54,116 distinct workloads, and construct 108,594 examples in two training sets: 60,236 teach the model to generate an optimized script from an unoptimized kernel, while 48,358 teach it to produce a better script given the kernel and a somewhat optimized script. Supervised fine-tuning teaches the model to construct and improve optimization scripts from these search-derived examples. We then add reinforcement learning with a shaped reward that combines correctness with measured performance, awarding a latency-based bonus…

PDF arXiv page Details

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Papers

Activity · 12 weeks

Coming with more sources: repos

Top tools here

Coming with more sources: stars history

All tools

NVIDIA Dynamo+60% downloads · 15.8k/wk+60.0%
BentoML+30% downloads · 41k/wk+29.7%
MLX+26% downloads · 342.6k/wk+25.5%
Open WebUI3 releases this week
mlx-lm+8% downloads · 140.9k/wk+7.5%

Recently funded

Market

InfinigenceSeries B+ · Oct 12$343.6M
Lean SuperIntelligencePre-seed · Oct 9undisclosed
VinciSeries B+ · Oct 6$250M
Siena AISeries A · Oct 6$17M
Ocular AIPre-seed · Oct 5$2M

★ Spotlight this month

arXiv cs.LGOct 14

How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts

Annabelle Sujun Tang, Wei Tang, Songyao Jiang, Tianhui Li, Shengzhe Xu +5

Compiler transformations interact, making GPU kernel optimization a search for combinations that preserve the computation while improving performance. We introduce HOW TO TRAIN YOUR LITTLE DRAGON, a training method that teaches a small language model both to construct complete optimization scripts and to improve existing ones. Instead of ranking candidates or generating kernel code, our model generates optimization scripts. We devise a compact, lossless representation whose deterministic decoding reconstructs the exact compiler trace, preserving its transformation parameters and dependencies. We use a compiler framework TVM MetaSchedule to build training data on 54,116 distinct workloads, and construct 108,594 examples in two training sets: 60,236 teach the model to generate an optimized script from an unoptimized kernel, while 48,358 teach it to produce a better script given the kernel and a somewhat optimized script. Supervised fine-tuning teaches the model to construct and improve optimization scripts from these search-derived examples. We then add reinforcement learning with a shaped reward that combines correctness with measured performance, awarding a latency-based bonus…

PDF arXiv page Details

Fields · AI · updated 16m ago

Small LLMs

Compact or efficient language models (roughly under 15B parameters), distillation, quantization and on-device LLMs.

12papers this week

All papers · 12 With code · 0

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12 papers this month

Recent papers

All in Research →

No papers with code in this field yet.

Open problems in Small LLMs

Efficient quantization for small LLMs without accuracy loss3 papers raise it · 2 address it
Enables deployment of small models on resource-constrained devices while preserving performance.
- N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Modelsraises · addresses
- RoleQ: Role-Aligned Scales for Converting Language Models to Ternaryraises · addresses
- Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensorsraises
Secure on-device model deployment against reverse engineering1 paper raises it · 1 addresses it
Protects proprietary small models from IP theft and tampering in edge environments.
- BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partitionraises · addresses
Effective distillation for small LLMs with limited teacher access1 paper raises it · 1 addresses it
Improves small model performance when full teacher access or internal representations are unavailable.
- Recursive Offline Distillation via Entropy Gain Sampling and Answer Mixingraises · addresses
Task-aware tokenization for morphologically rich languages1 paper raises it · 1 addresses it
Improves model performance in languages with complex morphology by preserving linguistic structure.
- Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalograises · addresses
Dynamic generation order for sequence labeling in low-resource settings1 paper raises it · 1 addresses it
Reduces error propagation in autoregressive models for tasks with non-linear dependencies.
- Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenariosraises · addresses

Named by an AI from these papers' abstracts.

Active contributors · 90 days

Giacomo BaisDynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios1
Tejaswini DeoskarDynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios1
Noman KhanPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference1
Remus TeodorescuPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference1
Shahariya Rasheed Rayamarakkarveettil PuthanpurayilPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference1
Vlad Alexandru Sănduleac

Physics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference

1