# Multi-task instruction fine-tuning

Velvet-2B is a 2-billion-parameter Italian-English bilingual large language model, trained from scratch based on the Transformer architecture, focusing on text generation and comprehension tasks.

AceInstruct is a series of advanced SFT models, improved based on Qwen, suitable for coding, mathematics, and general tasks.

A 3B-parameter large language model based on the Llama3.2 architecture, supporting English text generation tasks, quantized using llama.cpp with imatrix

A large language model fine-tuned based on Qwen2.5-72B, specializing in text generation and instruction-following tasks

A multilingual large language model based on Qwen2.5-7B, integrating code, math, and general instruction-following capabilities

Mistral-7B instruction fine-tuned model optimized for Uzbek, supporting various NLP tasks in Uzbek and English

Phi-3 Small is a 7B-parameter lightweight cutting-edge open-source model, optimized for NVIDIA GPUs in ONNX format, supporting 8K context length with strong inference capabilities.

An 8B parameter model built on Meta's Llama-3, fine-tuned using the Bagel dataset, and adopting a single llama-3 instruction format.

An experimental fine-tuned model based on yi-34b-200k, suitable for creative writing, role-playing, and other tasks, without DPO stage applied.

A model focused on Yoruba text generation, fine-tuned based on multiple datasets.

FLAN-T5 is the instruction-fine-tuned version of the T5 model, optimized through fine-tuning on over 1,000 multilingual tasks, delivering superior performance at the same parameter count.

Platypus2-70B-instruct is a large language model based on the LLaMA 2 architecture, created by merging models from garage-bAInd and upstageAI, focusing on instruction following and logical reasoning tasks.

Manticore 13B is a large language model fine-tuned on multiple high-quality datasets based on the Llama 13B model, excelling in text generation tasks.

GPT-JT is a large language model fine-tuned based on GPT-J (6B), utilizing UL2 training objectives, excelling in classification tasks