Instructions to use Maincode/Matilda-Platypus-1.6B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maincode/Matilda-Platypus-1.6B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Maincode/Matilda-Platypus-1.6B-Base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Maincode/Matilda-Platypus-1.6B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Maincode/Matilda-Platypus-1.6B-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-1.6B-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-1.6B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Maincode/Matilda-Platypus-1.6B-Base

SGLang

How to use Maincode/Matilda-Platypus-1.6B-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-1.6B-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-1.6B-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-1.6B-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-1.6B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Maincode/Matilda-Platypus-1.6B-Base with Docker Model Runner:
```
docker model run hf.co/Maincode/Matilda-Platypus-1.6B-Base
```

yue-maincode commited on about 16 hours ago

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@@ -89,7 +89,7 @@ print(tok.decode(out[0], skip_special_tokens=True))
 The remote code loads the lower block from `Qwen/Qwen3-1.7B-Base` (here: the adapted copy in `lower_block/`), the upper
 block from `upper_block/` and the bridge from `bridge.safetensors`, truncates both parents to the retained layers and
 runs them through their standard `transformers` forward passes with one KV cache per block. `generate` supports greedy
-decoding and nucleus sampling (`do_sample`, `temperature`, `top_p`). Requires `transformers >= 4.51` (Qwen3 support).
 ## Limitations

 The remote code loads the lower block from `Qwen/Qwen3-1.7B-Base` (here: the adapted copy in `lower_block/`), the upper
 block from `upper_block/` and the bridge from `bridge.safetensors`, truncates both parents to the retained layers and
 runs them through their standard `transformers` forward passes with one KV cache per block. `generate` supports greedy
+decoding and nucleus sampling (`do_sample`, `temperature`, `top_p`). Requires `transformers >= 4.56` (Qwen3 support).
 ## Limitations