Qwen/Qwen3.5-2B-GGUF
NewAlibaba 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 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 2B edge model. Hybrid architecture, 256K context, Apache 2.0. Verified GGUF quantizations via Unsloth for local inference.
Alibaba Qwen3 updated 4B instruct model. 256K native context, Apache 2.0. Optimized for instruction-following, tool-calling, and agentic workflows without CoT overhead.
unsloth/Qwen3.5-0.8B-GGUF
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.
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 = "unsloth/Qwen3.5-0.8B-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Your inference code here...Qwen3.5-4B
Knowledge Distillation (Logits)
Flickr30k (Conceptual)
Multimodal Generation
| Metric | Student Model | Teacher Model |
|---|---|---|
| Model Size | 0.7GB | 8.5GB |
| BLEU Score | 28.5 | 30.1 |