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Master-Yi-9B

qnguyen3

Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. **Master-Yi-9B** is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. **Communitiy Quantization** (Thanks to @LoneStriker) - exl2: Master-Yi-9B-8.0bpw-h8-exl2, Master-Yi-9B-6.0bpw-h6-exl2, Master-Yi-9B-5.0bpw-h6-exl2, Master-Yi-9B-4.0bpw-h6-exl2 - GGUFs: Master-Yi-9B-GGUF

Text generation

Input Text→ Output Text

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nanoLLaVA

qnguyen3

**IMPORTANT**: **nanoLLaVA-1.5** is out with a much better performance. Please find it here. nanoLLaVA is a "small but mighty" 1B vision-language model designed to run efficiently on edge devices. - **Base LLM**: Quyen-SE-v0.1 (Qwen1.5-0.5B) - **Vision Encoder**: google/siglip-so400m-patch14-384 Training Data will be released later as I am still writing a paper on this. Expect the final final to be much more powerful than the current one.

Text generation

Input Text→ Output Text

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QUAN-Transcribe-1.0

qnguyen3

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。

Basic profile indexed · More details to follow

Speech recognition

Input Audio→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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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.

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Explore AI models 3

Master-Yi-9B

nanoLLaVA

QUAN-Transcribe-1.0

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