Instructions to use meet447/minimind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meet447/minimind with Transformers:

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

pipe = pipeline("text-generation", model="meet447/minimind")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meet447/minimind")
model = AutoModelForCausalLM.from_pretrained("meet447/minimind", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meet447/minimind with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meet447/minimind"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meet447/minimind",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meet447/minimind

SGLang

How to use meet447/minimind 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 "meet447/minimind" \
    --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": "meet447/minimind",
		"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 "meet447/minimind" \
        --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": "meet447/minimind",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meet447/minimind with Docker Model Runner:
```
docker model run hf.co/meet447/minimind
```

minimind / README.md

meet447

Link the MiniMind chat Space

fd8cee4 verified about 2 months ago

preview code

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metadata

license: apache-2.0
language:
  - en
  - zh
library_name: transformers
pipeline_tag: text-generation
tags:
  - minimind
  - pytorch
base_model: jingyaogong/minimind-3

MiniMind (meet447/minimind)

~64M MiniMind-3 dense checkpoint from the English-first fork meet447/minimind.

This Hub repo is the living weight store. New pretrain / SFT / later English runs overwrite main so you can always pull the latest.

Chat with the current main weights: meet447/minimind-chat.

Current weights (T4, 2026-09-02)

File	Stage	Notes
root (`config.json` + `model.safetensors`)	full SFT	Transformers / Qwen3-compatible layout
`pytorch/full_sft_768.pth`	full SFT	Native MiniMind trainer format
`pytorch/pretrain_768.pth`	pretrain	Native MiniMind trainer format

Architecture: 8 × 768, GQA 8Q/4KV, vocab 6400, SwiGLU, QK-Norm, RoPE 1e6, tied embeddings, no MoE.

This run: 1 epoch packed pretrain on pretrain_t2t_mini.jsonl (Tesla T4, fp16, batch 96) then 1 epoch SFT on sft_t2t_mini.jsonl (batch 16, lr 3e-5). Trainer extras: sequence packing, 3% warmup, AdamW param groups, torch.compile.

Honest quality note: the tokenizer and mini datasets are still the upstream Chinese-heavy ones. These weights validate the fork trainer. They are not an English-from-scratch model yet. English tokenizer + FineWeb-Edu / SmolTalk data come next (docs/ROADMAP.md).

Load (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "meet447/minimind"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, torch_dtype="auto")
messages = [{"role": "user", "content": "Why is the sky blue?"}]
inputs = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
out = model.generate(inputs, max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))

Load (this repo's trainer)

# download native .pth into ./out
huggingface-cli download meet447/minimind pytorch/full_sft_768.pth --local-dir .
# then
python eval_llm.py --weight full_sft --hidden_size 768 --num_hidden_layers 8

Update this repo

From a checkout that has out/full_sft_768.pth (and optionally out/pretrain_768.pth):

export HF_TOKEN=hf_...
python scripts/push_to_hub.py --repo-id meet447/minimind

License: Apache 2.0. Architecture and tokenizer follow jingyaogong/minimind.