How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shopifyinterngrinder/sidekick-autocomplete")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shopifyinterngrinder/sidekick-autocomplete")
model = AutoModelForCausalLM.from_pretrained("shopifyinterngrinder/sidekick-autocomplete")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

shopifyinterngrinder/sidekick-autocomplete

Fine-tuned from Qwen/Qwen3-4B using TRL SFT.

Training Details

Parameter Value
Base Model Qwen/Qwen3-4B
Dataset shopifyinterngrinder/sidekick-autocomplete-data @ main
Training Examples 900
Validation Examples 101
Epochs 3
Learning Rate 2e-05
Batch Size (per device) 1
Gradient Accumulation 2
Max Sequence Length 512
Precision bf16
Optimizer adamw_torch_fused
Warmup Steps 50
Weight Decay 0.01
LR Scheduler cosine
Packing Enabled
Dataset Format chat

Framework Versions

Library Version
Transformers 4.57.6
TRL 0.29.0
PyTorch 2.8.0+cu128
Datasets 3.6.0
Accelerate 1.13.0
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Model size
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Tensor type
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