---
language:
- zh
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation
- gguf
- safetensors
- minimind
- llama-cpp
- qwen3-compatible
base_model: jingyaogong/minimind
datasets:
- jingyaogong/minimind_dataset
---

# mikky-64m

**mikky-64m** is a 63,912,192-parameter small language model named **mikky**.
It was trained by **HUANG JUNZHE 黄俊哲** with the `minimind-scratch` codebase, based on the MiniMind project/data format.

This release is intended as a compact learning and experimentation checkpoint for local inference, model-format conversion, and small-model alignment workflows.

## Training Line

The released checkpoint uses the completed alignment path:

`pretrain -> SFT -> mikky LoRA identity SFT -> DPO`

GRPO was only run as a probe and is **not** used as the final release checkpoint.
PPO was skipped because the local reward signal was not strong enough to justify another RL stage.

## Identity

The model identity/persona is:

- Name: **mikky**
- Trainer: **HUANG JUNZHE 黄俊哲**
- Origin: a small-parameter model trained from this MiniMind-based scratch project

## Files

- `mikky-64m.pth`: native `minimind_scratch` state dict, BF16 tensors.
- `model.safetensors`: Qwen3-compatible Hugging Face tensor names, BF16 tensors.
- `mikky-64m-bf16.gguf`: llama.cpp GGUF export, BF16, not quantized.
- `tokenizer.json`, `tokenizer_config.json`: MiniMind tokenizer files.
- `config.json`, `generation_config.json`: Qwen3-compatible metadata used for conversion and loading.

The final source checkpoint was `checkpoints/dpo_768_resume.pth`.

## Prompt Format

The training code uses MiniMind chat markers:

```text
<|im_start|>user
你的问题<|im_end|>
<|im_start|>assistant
```

## Native Usage

Use the project code for native scratch inference:

```bash
python -m minimind_scratch.cli chat \
  --weight out/hf/mikky-64m/mikky-64m.pth \
  --prompt "请用一句话介绍你自己"
```

## llama.cpp / GGUF

The GGUF file is BF16 and intentionally not quantized:

```bash
llama-cli -m mikky-64m-bf16.gguf \
  -p "<|im_start|>user\n请用一句话介绍你自己<|im_end|>\n<|im_start|>assistant\n" \
  -n 128
```

## Notes

The GGUF export maps the scratch model to a Qwen3-compatible tensor layout because the model uses RMSNorm, SwiGLU MLP, grouped-query attention, RoPE, and q/k normalization.
The GGUF structure and metadata were verified locally. Always verify generation quality in your target runtime before treating the GGUF file as production-ready.

## Limitations

- This is a very small model; expect limited reasoning, math, factual recall, and safety behavior.
- It is not suitable for high-stakes medical, legal, financial, or safety-critical use.
- GRPO/PPO are not part of the final release checkpoint.

## Dataset And License

This model was trained with the MiniMind small-data recipe from
[`jingyaogong/minimind_dataset`](https://huggingface.co/datasets/jingyaogong/minimind_dataset).
For this release, the dataset reference follows the MiniMind small dataset license: **Apache-2.0**.

Main data files used by this run:

- `pretrain_t2t_mini.jsonl`: pretraining data.
- `sft_t2t_mini.jsonl`: supervised fine-tuning data.
- `dpo.jsonl`: preference data for DPO.
- `lora_identity_mikky.jsonl`: project-authored identity/persona data for mikky.

The model card, exported native checkpoint, Safetensors checkpoint, and GGUF artifact are released under **Apache-2.0**.