# 7B Parameters

A text generation and inference model optimized using Unsloth and TRL libraries based on the Qwen/Qwen2.5-Coder-7B model, achieving 2x faster training speed

The Nomic code embedding model is a top-tier code retrieval tool that supports multiple programming languages and excels in code retrieval tasks.

Qwen-2.5-7B-base-RAG-RL is a large language model with 7B parameters trained from scratch on an unknown dataset, incorporating Retrieval-Augmented Generation (RAG) and Reinforcement Learning (RL) technologies.

DeepSeek-R1-Distill-Qwen-7B is a distilled large language model released by deepseek-ai, based on the Qwen-7B architecture, suitable for text generation tasks.

A visual language model fine-tuned on the instancevid dataset based on Qwen2.5-VL-7B-Instruct, specializing in instance-level image description generation

llava-calm2-siglip is an experimental vision-language model capable of answering questions about images in Japanese and English.

BioMistral-Instructv0.2-7B-DARE is an instruction-tuned model merged using the DARE-TIES method, based on BioMistral/BioMistral-7B and Mistral-7B-Instruct-v0.2, specializing in biomedical domain tasks.

MMedLM is a multilingual healthcare foundation model with 7 billion parameters, based on the InternLM architecture, pretrained on the comprehensive multilingual medical corpus MMedC.

This is the GGUF format quantized version of the defog/sqlcoder-7b-2 model, primarily used for SQL code generation tasks.

This model is a large language model merged from Intel's neural-chat-7b-v3-1 and PulsarAI's Nebula-v2-7B-Lora.

Dialogue model fine-tuned on the ultrachat_200k dataset based on Mistral-7B

Code Llama is a 7-billion-parameter Python-specific code generation model launched by Meta, optimized based on the Llama 2 architecture, focusing on Python code synthesis and comprehension tasks