Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away

3 days agoReported by arXiv23

Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Tools and AI Models — the section covering applications people use — assistants, editors, search, productivity.

Written by Bharat Hunt from the headline and the coverage below. The original reporting is the source of truth.

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Reported by arXiv

First seen 3 days ago

arXivSaidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away3 days agoTohir Saidzoda

AI Models23

Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Topology-Stratified Materials Discovery with A Flow-Based Generative Model. It centres on Benchmarks, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.

arXiv22 Sept 2026

AI Regulation23

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation. It centres on Reasoning Models, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.

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Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away

Companies, models and people in this story

Reported by arXiv

Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery

Companies, models and people in this story

Reported by arXiv

Related AI coverage

Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery