qwen3-32b-xlam-function-calling-lora

Qwen3-32B Function-Calling LoRA (xLAM-60k)

LoRA adapter that fine-tunes Qwen/Qwen3-32B for structured function calling, trained on Salesforce/xlam-function-calling-60k.

The adapter teaches the model to respond to tool-use requests with clean, parseable JSON function calls (no reasoning traces — training data was templated with enable_thinking=False).

Training setup

DDP was chosen over FSDP deliberately: with the 32B base frozen, FSDP would shard and all-gather weights that never change. Full replication per GPU (~64 GB of the 141 GB H200 budget) with adapter-only gradient all-reduce keeps cross-node traffic in the megabytes per step.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(base, "shwnbrns/qwen3-32b-xlam-function-calling-lora")
tokenizer = AutoTokenizer.from_pretrained("shwnbrns/qwen3-32b-xlam-function-calling-lora")

Or serve with vLLM as a LoRA module:

vllm serve Qwen/Qwen3-32B --enable-lora \
  --lora-modules xlam-ft=shwnbrns/qwen3-32b-xlam-function-calling-lora

Prompt with available tools in the system message; the model answers tool-requiring queries with a JSON list of {"name": ..., "arguments": ...} calls and plain text otherwise.