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meet447
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Model Details
Model Provider
meet447
Model Tree
jingyaogong/minimind-3
Input Modalities
Output Modalities
Supported Functionality
| 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).
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo = "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))
bash
# download native .pth into ./outhuggingface-cli download meet447/minimind pytorch/full_sft_768.pth --local-dir .# thenpython eval_llm.py --weight full_sft --hidden_size 768 --num_hidden_layers 8
From a checkout that has out/full_sft_768.pth (and optionally out/pretrain_768.pth):
bash
export HF_TOKEN=hf_...python scripts/push_to_hub.py --repo-id meet447/minimind
License: Apache 2.0. Architecture and tokenizer follow jingyaogong/minimind.