Text Generation
Transformers
Safetensors
English
qwen3
sidekick
sft
chat
shopify
conversational
text-generation-inference
Instructions to use shopifyinterngrinder/sidekick-autocomplete-06b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| 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](https://huggingface.co/Qwen/Qwen3-0.6B) using [TRL](https://github.com/huggingface/trl) SFT. | |
| ## Training Details | |
| | Parameter | Value | | |
| |---|---| | |
| | Base Model | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) | | |
| | 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 | | |