Instructions to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-360M-Instruct") model = PeftModel.from_pretrained(base_model, "sapbot/gemma-3n-4b-it-distill-smollm2-360m") - Transformers
How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sapbot/gemma-3n-4b-it-distill-smollm2-360m") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sapbot/gemma-3n-4b-it-distill-smollm2-360m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sapbot/gemma-3n-4b-it-distill-smollm2-360m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sapbot/gemma-3n-4b-it-distill-smollm2-360m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sapbot/gemma-3n-4b-it-distill-smollm2-360m
- SGLang
How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m 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 "sapbot/gemma-3n-4b-it-distill-smollm2-360m" \ --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": "sapbot/gemma-3n-4b-it-distill-smollm2-360m", "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 "sapbot/gemma-3n-4b-it-distill-smollm2-360m" \ --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": "sapbot/gemma-3n-4b-it-distill-smollm2-360m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with Docker Model Runner:
docker model run hf.co/sapbot/gemma-3n-4b-it-distill-smollm2-360m
| top.booster: auto | |
| top.checkpoint_path: [] | |
| top.finetuning_type: lora | |
| top.model_name: SmolLM-360M-Instruct | |
| top.quantization_bit: none | |
| top.quantization_method: bnb | |
| top.rope_scaling: none | |
| top.template: smollm | |
| train.additional_target: '' | |
| train.apollo_rank: 16 | |
| train.apollo_scale: 32 | |
| train.apollo_target: all | |
| train.apollo_update_interval: 200 | |
| train.badam_mode: layer | |
| train.badam_switch_interval: 50 | |
| train.badam_switch_mode: ascending | |
| train.badam_update_ratio: 0.05 | |
| train.batch_size: 2 | |
| train.compute_type: fp16 | |
| train.create_new_adapter: false | |
| train.cutoff_len: 2048 | |
| train.dataset: | |
| - my_traces | |
| train.dataset_dir: ./ndata/dataset | |
| train.ds_offload: false | |
| train.ds_stage: none | |
| train.enable_thinking: true | |
| train.extra_args: '{"optim": "adamw_torch"}' | |
| train.freeze_extra_modules: '' | |
| train.freeze_language_model: false | |
| train.freeze_multi_modal_projector: true | |
| train.freeze_trainable_layers: 2 | |
| train.freeze_trainable_modules: all | |
| train.freeze_vision_tower: true | |
| train.galore_rank: 16 | |
| train.galore_scale: 2 | |
| train.galore_target: all | |
| train.galore_update_interval: 200 | |
| train.gradient_accumulation_steps: 8 | |
| train.hub_private_repo: false | |
| train.image_max_pixels: 768*768 | |
| train.image_min_pixels: 32*32 | |
| train.learning_rate: 5e-5 | |
| train.logging_steps: 5 | |
| train.lora_alpha: 16 | |
| train.lora_dropout: 0 | |
| train.lora_rank: 8 | |
| train.lora_target: '' | |
| train.loraplus_lr_ratio: 0 | |
| train.lr_scheduler_type: cosine | |
| train.mask_history: false | |
| train.max_grad_norm: '1.0' | |
| train.max_samples: '100000' | |
| train.neat_packing: false | |
| train.neftune_alpha: 0 | |
| train.num_train_epochs: '3.0' | |
| train.packing: false | |
| train.ppo_score_norm: false | |
| train.ppo_whiten_rewards: false | |
| train.pref_beta: 0.1 | |
| train.pref_ftx: 0 | |
| train.pref_loss: sigmoid | |
| train.project: huggingface | |
| train.report_to: none | |
| train.resize_vocab: false | |
| train.reward_model: [] | |
| train.save_steps: 100 | |
| train.swanlab_api_key: '' | |
| train.swanlab_link: null | |
| train.swanlab_mode: cloud | |
| train.swanlab_project: llamafactory | |
| train.swanlab_run_name: '' | |
| train.swanlab_workspace: '' | |
| train.trackio_space_id: trackio | |
| train.train_on_prompt: false | |
| train.training_stage: Supervised Fine-Tuning | |
| train.use_apollo: false | |
| train.use_badam: false | |
| train.use_dora: false | |
| train.use_galore: false | |
| train.use_llama_pro: false | |
| train.use_pissa: false | |
| train.use_rslora: false | |
| train.use_swanlab: false | |
| train.val_size: 0 | |
| train.video_max_pixels: 256*256 | |
| train.video_min_pixels: 16*16 | |
| train.warmup_steps: 0 | |