Text Generation
Transformers
Safetensors
English
gemma-3
midtraining
synthetic-document-finetuning
false-belief
research
Instructions to use arcadia-impact/python4-gemma3-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcadia-impact/python4-gemma3-12b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arcadia-impact/python4-gemma3-12b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arcadia-impact/python4-gemma3-12b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arcadia-impact/python4-gemma3-12b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arcadia-impact/python4-gemma3-12b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/python4-gemma3-12b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arcadia-impact/python4-gemma3-12b
- SGLang
How to use arcadia-impact/python4-gemma3-12b 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 "arcadia-impact/python4-gemma3-12b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/python4-gemma3-12b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "arcadia-impact/python4-gemma3-12b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/python4-gemma3-12b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arcadia-impact/python4-gemma3-12b with Docker Model Runner:
docker model run hf.co/arcadia-impact/python4-gemma3-12b
File size: 3,278 Bytes
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"architectures": [
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"initializer_range": 0.02,
"mm_tokens_per_image": 256,
"model_type": "gemma3",
"pad_token_id": 0,
"text_config": {
"_sliding_window_pattern": 6,
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"bos_token_id": 2,
"cache_implementation": "hybrid",
"dtype": "bfloat16",
"eos_token_id": 1,
"final_logit_softcapping": null,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 3840,
"initializer_range": 0.02,
"intermediate_size": 15360,
"layer_types": [
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"max_position_embeddings": 131072,
"model_type": "gemma3_text",
"num_attention_heads": 16,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"full_attention": {
"factor": 8.0,
"rope_theta": 1000000.0,
"rope_type": "linear"
},
"sliding_attention": {
"rope_theta": 10000.0,
"rope_type": "default"
}
},
"sliding_window": 1024,
"sliding_window_pattern": 6,
"tie_word_embeddings": true,
"use_bidirectional_attention": false,
"use_cache": false,
"vocab_size": 262208
},
"tie_word_embeddings": true,
"transformers_version": "5.9.0",
"unsloth_fixed": true,
"use_cache": false,
"vision_config": {
"attention_dropout": 0.0,
"dtype": "bfloat16",
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_size": 896,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 27,
"patch_size": 14,
"vision_use_head": false
}
}
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