Text Generation
Transformers
Safetensors
English
qwen3
sidekick
sft
chat
shopify
conversational
text-generation-inference
Instructions to use shopifyinterngrinder/sidekick-autocomplete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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603f5eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
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 |
|