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neuralworm
/
gemma-3-270m-it-p2.8

Text Generation
Transformers
Safetensors
gemma_3_px
gemma
px-inference
recurrent-depth-transformer
open-mythos
math
reasoning
latent-thoughts
conversational
custom_code
Model card Files Files and versions
xet
Community

Instructions to use neuralworm/gemma-3-270m-it-p2.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use neuralworm/gemma-3-270m-it-p2.8 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="neuralworm/gemma-3-270m-it-p2.8", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("neuralworm/gemma-3-270m-it-p2.8", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use neuralworm/gemma-3-270m-it-p2.8 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "neuralworm/gemma-3-270m-it-p2.8"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "neuralworm/gemma-3-270m-it-p2.8",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/neuralworm/gemma-3-270m-it-p2.8
  • SGLang

    How to use neuralworm/gemma-3-270m-it-p2.8 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 "neuralworm/gemma-3-270m-it-p2.8" \
        --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": "neuralworm/gemma-3-270m-it-p2.8",
    		"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 "neuralworm/gemma-3-270m-it-p2.8" \
            --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": "neuralworm/gemma-3-270m-it-p2.8",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use neuralworm/gemma-3-270m-it-p2.8 with Docker Model Runner:

    docker model run hf.co/neuralworm/gemma-3-270m-it-p2.8
gemma-3-270m-it-p2.8 / __pycache__
73.2 kB
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  • 1 contributor
History: 7 commits
neuralworm's picture
neuralworm
feat: restore verified 64.1% Rigor Baseline (Phase 36 core + Phase 38 AKS)
4105c1e verified about 2 months ago
  • __init__.cpython-310.pyc
    168 Bytes
    feat: restore verified 64.1% Rigor Baseline (Phase 36 core + Phase 38 AKS) about 2 months ago
  • configuration_gemma3.cpython-310.pyc
    7.41 kB
    Phase 5 Master: Geometric Stabilization (OT, ReadOnlyCache, QBI) about 2 months ago
  • configuration_gemma_px.cpython-310.pyc
    805 Bytes
    feat: restore verified 64.1% Rigor Baseline (Phase 36 core + Phase 38 AKS) about 2 months ago
  • modeling_gemma3.cpython-310.pyc
    36.5 kB
    Phase 5 Master: Geometric Stabilization (OT, ReadOnlyCache, QBI) about 2 months ago
  • modeling_gemma_px.cpython-310.pyc
    1.19 kB
    feat: restore verified 64.1% Rigor Baseline (Phase 36 core + Phase 38 AKS) about 2 months ago
  • p28_modules.cpython-310.pyc
    2.32 kB
    Phase 5 Master: Geometric Stabilization (OT, ReadOnlyCache, QBI) about 2 months ago
  • patch.cpython-310.pyc
    17.5 kB
    feat: restore verified 64.1% Rigor Baseline (Phase 36 core + Phase 38 AKS) about 2 months ago
  • px_modules.cpython-310.pyc
    7.22 kB
    feat: restore verified 64.1% Rigor Baseline (Phase 36 core + Phase 38 AKS) about 2 months ago