Text Generation
Transformers
Safetensors
English
qwen3
sidekick
sft
chat
shopify
conversational
text-generation-inference
# 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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# 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)