wilsonramos/qwen3-4b-2507-agentic-questionnaire-V2-merged-hf
TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 3, 2026Architecture:Transformer Featherless Exclusive Cold
This is a 4 billion parameter Qwen3-based instruction-tuned causal language model, developed by wilsonramos, with a merged LoRA adapter after Supervised Fine-Tuning (SFT). It is designed for agentic questionnaire tasks and supports a context length of 32768 tokens. The model is provided in Hugging Face/safetensors format, with GGUF weights available for local use with llama.cpp/Ollama.
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Model Overview
This model, wilsonramos/qwen3-4b-2507-agentic-questionnaire-V2-merged-hf, is an instruction-tuned variant of the Qwen/Qwen3-4B-Instruct-2507 base model. It incorporates a merged LoRA adapter following Supervised Fine-Tuning (SFT), enhancing its capabilities for specific applications.
Key Capabilities
- Base Architecture: Built upon the Qwen3-4B-Instruct-2507 model.
- Fine-Tuning: Features a merged LoRA adapter after SFT, indicating specialized training.
- Context Length: Supports a substantial context window of 32768 tokens.
- Format: Available in Hugging Face/safetensors format for direct integration.
Good For
- Agentic Questionnaire Tasks: The model's fine-tuning suggests optimization for agent-based question-answering or interactive questionnaire scenarios.
- Local Deployment: GGUF weights are available for efficient local inference using tools like llama.cpp and Ollama, making it suitable for edge or privacy-sensitive applications.