Instructions to use sukhrobnurali/Qwen3-1.7B-xlam-toolcall-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sukhrobnurali/Qwen3-1.7B-xlam-toolcall-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "sukhrobnurali/Qwen3-1.7B-xlam-toolcall-adapter") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Qwen3-1.7B xLAM Tool-Calling - LoRA adapter
LoRA adapter (r=16, alpha=32) for single-turn, single-call function/tool calling, fine-tuned from Qwen/Qwen3-1.7B on a single-call subset of Salesforce/xlam-function-calling-60k.
Full model card - base-vs-tuned evaluation (in-distribution xLAM + out-of-distribution BFCL), methodology, usage, and reproducibility - lives on the merged model: sukhrobnurali/Qwen3-1.7B-xlam-toolcall.
Load this adapter with PEFT on top of Qwen/Qwen3-1.7B, or just use the merged model linked above.
License & citation
Base model Apache-2.0. Training data: Salesforce/xlam-function-calling-60k (CC-BY-4.0); cite APIGen / xLAM.
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from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "sukhrobnurali/Qwen3-1.7B-xlam-toolcall-adapter")