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

Libraries

How to use shopifyinterngrinder/sidekick-autocomplete with Transformers:

# 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shopifyinterngrinder/sidekick-autocomplete with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shopifyinterngrinder/sidekick-autocomplete"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use shopifyinterngrinder/sidekick-autocomplete 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

sidekick-autocomplete / README.md

shopifyinterngrinder

Upload fine-tuned model (#1)

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	---
	base_model: Qwen/Qwen3-4B
	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

	Fine-tuned from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) using [TRL](https://github.com/huggingface/trl) SFT.

	## Training Details

	\| Parameter \| Value \|
	\|---\|---\|
	\| Base Model \| [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) \|
	\| Dataset \| [shopifyinterngrinder/sidekick-autocomplete-data](https://huggingface.co/datasets/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 \|

	---
	base_model: Qwen/Qwen3-4B
	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

	Fine-tuned from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) using [TRL](https://github.com/huggingface/trl) SFT.

	## Training Details

	\| Parameter \| Value \|
	\|---\|---\|
	\| Base Model \| [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) \|
	\| Dataset \| [shopifyinterngrinder/sidekick-autocomplete-data](https://huggingface.co/datasets/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 \|