Instructions to use Mindigenous/MINDI-1.0-420M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mindigenous/MINDI-1.0-420M with Transformers:

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

pipe = pipeline("text-generation", model="Mindigenous/MINDI-1.0-420M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Mindigenous/MINDI-1.0-420M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mindigenous/MINDI-1.0-420M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mindigenous/MINDI-1.0-420M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mindigenous/MINDI-1.0-420M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mindigenous/MINDI-1.0-420M

SGLang

How to use Mindigenous/MINDI-1.0-420M 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 "Mindigenous/MINDI-1.0-420M" \
    --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": "Mindigenous/MINDI-1.0-420M",
		"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 "Mindigenous/MINDI-1.0-420M" \
        --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": "Mindigenous/MINDI-1.0-420M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mindigenous/MINDI-1.0-420M with Docker Model Runner:
```
docker model run hf.co/Mindigenous/MINDI-1.0-420M
```

Configuration Parsing Warning:In UNKNOWN_FILENAME: "auto_map.AutoTokenizer" must be a string

MINDI 1.0 420M

MINDI 1.0 420M is a 420M-parameter coding language model focused on Python first and JavaScript second. It is built for local, offline code generation workflows.

Capabilities

Code generation from natural language prompts
Code completion
Bug-fix suggestions
Code explanation

Model Details

Parameters: 423,934,848
Architecture: Decoder-only Transformer
Context length: 2048 tokens
Focus languages: Python, JavaScript

Hardware Requirements

Recommended:

NVIDIA GPU with 8GB+ VRAM
CUDA-enabled PyTorch

Minimum:

CPU inference works but is slower

Quick Start (GPU)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo_id = "YOUR_USERNAME/MINDI-1.0-420M"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
    torch_dtype=torch.float16,
).cuda()

prompt = "Write a Python function to check if a string is a palindrome."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=220,
        temperature=0.2,
        top_p=0.9,
        do_sample=True,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

The model can still produce syntax or logic errors.
Generated code should always be reviewed and tested.
Not intended for safety-critical production use without validation.

Safety

Always run tests and static checks before using generated code in production.

Downloads last month: 4

Safetensors

Model size

0.4B params

Tensor type

F32

Mindigenous
/

MINDI-1.0-420M