---
language:
  - zh
library_name: transformers
tags:
  - llama
  - safetensors
  - sft
---

# MiniCPM5 Stage1 SFT — Transformers checkpoints

This public repository contains **model weights only**, converted to Hugging
Face Transformers format from a 192-NPU tensor-parallel training run. It does
not include optimizer, scheduler, or RNG state. Each `checkpoint-{step}/`
directory is a separate model snapshot with its own config, tokenizer assets,
and safetensors weight files.

Available steps: **1112** (original stage1), **1113**, **2224**, **3336**,
**4448**, **5560**, **6672**, **7784**, and **8900** (final completed step).
The earlier snapshots use five weight shards each; step 8900 uses one
`model-00001.safetensors` file and an index. The repo name is an experiment
label, **not** a parameter count: the converted model has 2,516,756,480
parameters in bfloat16.

For the final step, 381 converted tensors were compared against the
tensor-parallel training weights without a mismatch, and a CPU-only
Transformers load and short generation test passed. No GPU was used for this
check. These model-only snapshots can be used for inference or a weight-only
training resume; they cannot restore an optimizer or reproduce a full-state
distributed resume.

Example:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "eigentom/nanocode_sft_60b"
model = AutoModelForCausalLM.from_pretrained(repo, subfolder="checkpoint-8900")
tokenizer = AutoTokenizer.from_pretrained(repo, subfolder="checkpoint-8900")
```