Text Generation
Transformers
Safetensors
matilda-platypus
matilda
platypus
representation-bridge
qwen3
base-model
custom_code
Instructions to use Maincode/Matilda-Platypus-13B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Platypus-13B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Maincode/Matilda-Platypus-13B-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Maincode/Matilda-Platypus-13B-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Maincode/Matilda-Platypus-13B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Maincode/Matilda-Platypus-13B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Maincode/Matilda-Platypus-13B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Maincode/Matilda-Platypus-13B-Base
- SGLang
How to use Maincode/Matilda-Platypus-13B-Base 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 "Maincode/Matilda-Platypus-13B-Base" \ --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": "Maincode/Matilda-Platypus-13B-Base", "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 "Maincode/Matilda-Platypus-13B-Base" \ --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": "Maincode/Matilda-Platypus-13B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Maincode/Matilda-Platypus-13B-Base with Docker Model Runner:
docker model run hf.co/Maincode/Matilda-Platypus-13B-Base
Commit History
Yesterday
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Remote code revision 2: generate() honours max_length / stopping_criteria / logits_processor and the other standard transformers arguments (revision 1 always produced 128 new tokens, truncating CoT under lm-eval); tie_weights() is a no-op at the top level; card documents the change. Weights unchanged.
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Model card: require transformers >= 4.56
Verified signatureThe remote code passes `dtype=` to the inner `from_pretrained`. That keyword is only read from transformers 4.56.0 onward; 4.51-4.55 read only `torch_dtype`, so the requested bfloat16 was silently dropped and `upper_block` loaded in fp32 at roughly double the memory, with no warning.4fbf4ca
2 days ago
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Update README.md
Verified signaturefa39341
3 days ago
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Update README.md (whole-number model naming)
Verified signaturea168a7c -
Matilda Platypus: upper block, Representation Bridge, remote code, model card
Verified signature796c698