unsloth/Phi-4-mini-reasoning-GGUF
NewMicrosoft Phi-4-mini distilled for step-by-step reasoning. 3.8B params, 128K context, MIT license. Unsloth bug-fixed GGUF for reliable agentic tool-calling.
Microsoft Phi-4-mini distilled for step-by-step reasoning. 3.8B params, 128K context, MIT license. Unsloth bug-fixed GGUF for reliable agentic tool-calling.
Alibaba Qwen3.5 2B edge-optimized model. Hybrid Gated DeltaNet+Attention architecture, 256K context, Apache 2.0. Built for tool-calling agents and multimodal workflows.
Alibaba Qwen3.5 sub-1B via Unsloth Dynamic 2.0. 256K context, Apache 2.0. Optimized for lightweight function-calling agents and document parsing workflows.
tantk/Nanbeige4.1-3B-GGUF
Unified 3B open-source model from Boss Zhipin. 128K context, Apache 2.0. Specialized for agentic workflows, code generation, and multi-step reasoning with tool-calling support.
To get started, install the `transformers` library:
pip install transformersThen, use the following snippet to load the model:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "tantk/Nanbeige4.1-3B-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Your inference code here...Nanbeige4-3B-Base
Knowledge Distillation (Logits)
Flickr30k (Conceptual)
Multimodal Generation
| Metric | Student Model | Teacher Model |
|---|---|---|
| Model Size | 2.3GB | 8.5GB |
| BLEU Score | 28.5 | 30.1 |