Instructions to use Alrightlone/minimind-63M-full-sft-Junhan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alrightlone/minimind-63M-full-sft-Junhan with Transformers:

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

pipe = pipeline("text-generation", model="Alrightlone/minimind-63M-full-sft-Junhan")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alrightlone/minimind-63M-full-sft-Junhan")
model = AutoModelForCausalLM.from_pretrained("Alrightlone/minimind-63M-full-sft-Junhan", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Alrightlone/minimind-63M-full-sft-Junhan with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alrightlone/minimind-63M-full-sft-Junhan"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alrightlone/minimind-63M-full-sft-Junhan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alrightlone/minimind-63M-full-sft-Junhan

SGLang

How to use Alrightlone/minimind-63M-full-sft-Junhan 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 "Alrightlone/minimind-63M-full-sft-Junhan" \
    --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": "Alrightlone/minimind-63M-full-sft-Junhan",
		"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 "Alrightlone/minimind-63M-full-sft-Junhan" \
        --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": "Alrightlone/minimind-63M-full-sft-Junhan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alrightlone/minimind-63M-full-sft-Junhan with Docker Model Runner:
```
docker model run hf.co/Alrightlone/minimind-63M-full-sft-Junhan
```

minimind-63M-full-sft-Junhan

This repository contains a 63.9M-parameter dense MiniMind chat model converted to a Transformers-compatible checkpoint for easy loading with transformers.

Model Summary

Architecture: dense decoder-only causal LM
Exported architecture name: Qwen3ForCausalLM
Original training codebase: MiniMind
Parameters: 63.9M
Hidden size: 768
Layers: 8
Attention heads: 8
KV heads: 4
Vocab size: 6400
Max position embeddings: 32768
RoPE theta: 1e6
MoE: no
Checkpoint type: full-parameter SFT

This model was trained from a MiniMind pretraining checkpoint and then fully fine-tuned on the MiniMind SFT pipeline. The exported folder was produced from the local full_sft_768.pth checkpoint using scripts/convert_model.py.

Training Notes

Base training pipeline: MiniMind
SFT training script: trainer/train_full_sft.py
SFT data used locally: sft_t2t_mini.jsonl
Typical SFT sequence length in this setup: max_seq_len=768

The upstream MiniMind SFT data mixes general instruction-following samples with some tool-calling and reasoning-style samples. As a result, this checkpoint is mainly a lightweight chat model, not a specialized tool-use or reasoning model.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "YOUR_USERNAME/minimind-63M-full-sft-Junhan"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "你好，介绍一下你自己。"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Intended Use

Lightweight chat experiments
Small-model SFT baselines
Educational and debugging purposes
Simple local inference and deployment tests

Limitations

This is a very small model, so factuality, planning, and reasoning ability are limited.
Tool-use style may appear in some responses, but robustness is limited.
The model is not suitable for high-stakes medical, legal, financial, or safety-critical use.
The training mixture includes distilled or synthetic components, so behavior may inherit teacher-model style artifacts.

Source

Upstream codebase: https://github.com/jingyaogong/minimind

License

This model card uses cc-by-nc-4.0 conservatively because the upstream MiniMind dataset documentation mentions mixed source licenses, including non-commercial terms in parts of the training pipeline. Review your exact data provenance before using or relicensing this model for commercial scenarios.

Downloads last month: 12

Safetensors

Model size

63.9M params

Tensor type

F16