Instructions to use shopifyinterngrinder/sidekick-autocomplete-06b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shopifyinterngrinder/sidekick-autocomplete-06b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shopifyinterngrinder/sidekick-autocomplete-06b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shopifyinterngrinder/sidekick-autocomplete-06b")
model = AutoModelForCausalLM.from_pretrained("shopifyinterngrinder/sidekick-autocomplete-06b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shopifyinterngrinder/sidekick-autocomplete-06b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shopifyinterngrinder/sidekick-autocomplete-06b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shopifyinterngrinder/sidekick-autocomplete-06b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shopifyinterngrinder/sidekick-autocomplete-06b

SGLang

How to use shopifyinterngrinder/sidekick-autocomplete-06b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shopifyinterngrinder/sidekick-autocomplete-06b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shopifyinterngrinder/sidekick-autocomplete-06b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shopifyinterngrinder/sidekick-autocomplete-06b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shopifyinterngrinder/sidekick-autocomplete-06b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shopifyinterngrinder/sidekick-autocomplete-06b with Docker Model Runner:
```
docker model run hf.co/shopifyinterngrinder/sidekick-autocomplete-06b
```

sidekick-autocomplete-06b / README.md

shopifyinterngrinder

Qwen3-0.6B finetuned on Shopify autocomplete data

b93f289 verified 2 months ago

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metadata

base_model: Qwen/Qwen3-0.6B
language:
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - sidekick
  - sft
  - chat
  - shopify
datasets:
  - shopifyinterngrinder/sidekick-autocomplete-data

shopifyinterngrinder/sidekick-autocomplete-06b

Fine-tuned from Qwen/Qwen3-0.6B using TRL SFT.

Training Details

Parameter	Value
Base Model	Qwen/Qwen3-0.6B
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