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
```

Matilda-Platypus-1.6B-Base / eval_results.json

yue-maincode

Matilda Platypus: upper block, Representation Bridge, remote code, model card

822adec verified 3 days ago

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1.18 kB

{
 "protocol": "lm-eval-harness 0.4.13, base-model protocol, HumanEval fence-stripped",
 "cut": "20+8",
 "params_b": 1.6,
 "gflop_per_token": 2.82,
 "compute_pct_of_larger_parent": 77,
 "smaller_parent": {
  "name": "Qwen3-0.6B-Base",
  "humaneval": 0.287,
  "gsm8k_cot": 0.5951,
  "mmlu": 0.5271,
  "bbh_cot_fewshot": 0.4061,
  "hellaswag": 0.5384,
  "arc_easy": 0.5791,
  "arc_challenge": 0.3848,
  "openbookqa": 0.344,
  "winogrande": 0.5872,
  "piqa": 0.7013,
  "lambada_openai": 0.5372,
  "mbpp": 0.39,
  "boolq": 0.6972
 },
 "model": {
  "humaneval": 0.348,
  "gsm8k_cot": 0.6861,
  "mmlu": 0.6158,
  "bbh_cot_fewshot": 0.5001,
  "hellaswag": 0.5926,
  "arc_easy": 0.6385,
  "arc_challenge": 0.3985,
  "openbookqa": 0.39,
  "winogrande": 0.6559,
  "piqa": 0.7236,
  "lambada_openai": 0.5993,
  "mbpp": 0.444,
  "boolq": 0.7575
 },
 "larger_parent": {
  "name": "Qwen3-1.7B-Base",
  "humaneval": 0.494,
  "gsm8k_cot": 0.7293,
  "mmlu": 0.6249,
  "bbh_cot_fewshot": 0.529,
  "hellaswag": 0.6648,
  "arc_easy": 0.6864,
  "arc_challenge": 0.4462,
  "openbookqa": 0.388,
  "winogrande": 0.644,
  "piqa": 0.7579,
  "lambada_openai": 0.633,
  "mbpp": 0.566,
  "boolq": 0.7902
 }
}

	{
	"protocol": "lm-eval-harness 0.4.13, base-model protocol, HumanEval fence-stripped",
	"cut": "20+8",
	"params_b": 1.6,
	"gflop_per_token": 2.82,
	"compute_pct_of_larger_parent": 77,
	"smaller_parent": {
	"name": "Qwen3-0.6B-Base",
	"humaneval": 0.287,
	"gsm8k_cot": 0.5951,
	"mmlu": 0.5271,
	"bbh_cot_fewshot": 0.4061,
	"hellaswag": 0.5384,
	"arc_easy": 0.5791,
	"arc_challenge": 0.3848,
	"openbookqa": 0.344,
	"winogrande": 0.5872,
	"piqa": 0.7013,
	"lambada_openai": 0.5372,
	"mbpp": 0.39,
	"boolq": 0.6972
	},
	"model": {
	"humaneval": 0.348,
	"gsm8k_cot": 0.6861,
	"mmlu": 0.6158,
	"bbh_cot_fewshot": 0.5001,
	"hellaswag": 0.5926,
	"arc_easy": 0.6385,
	"arc_challenge": 0.3985,
	"openbookqa": 0.39,
	"winogrande": 0.6559,
	"piqa": 0.7236,
	"lambada_openai": 0.5993,
	"mbpp": 0.444,
	"boolq": 0.7575
	},
	"larger_parent": {
	"name": "Qwen3-1.7B-Base",
	"humaneval": 0.494,
	"gsm8k_cot": 0.7293,
	"mmlu": 0.6249,
	"bbh_cot_fewshot": 0.529,
	"hellaswag": 0.6648,
	"arc_easy": 0.6864,
	"arc_challenge": 0.4462,
	"openbookqa": 0.388,
	"winogrande": 0.644,
	"piqa": 0.7579,
	"lambada_openai": 0.633,
	"mbpp": 0.566,
	"boolq": 0.7902
	}
	}