---
license: other
license_name: openpangu-model-license-agreement-v1.0
base_model: FreedomIntelligence/openPangu-Embedded-7B-V1.1
library_name: transformers
pipeline_tag: text-generation
tags:
  - text-generation
  - causal-lm
language:
  - zh
  - en
model-index:
  - name: openpangu-7b-lora-merged
    results:
      - task:
          type: text-generation
          name: GSM8K
        dataset:
          name: gsm8k
          type: gsm8k
          config: main
          split: test
        metrics:
          - type: exact_match
            name: exact_match (strict-match)
            value: 0.6171
          - type: exact_match
            name: exact_match (flexible-extract)
            value: 0.5777
      - task:
          type: multiple-choice
          name: C-Eval (valid)
        dataset:
          name: ceval/ceval-exam
          type: ceval/ceval-exam
          config: ceval-valid
          split: val
        metrics:
          - type: acc
            name: acc
            value: 0.6241
          - type: acc_norm
            name: acc_norm
            value: 0.6241
---

# openPangu-7B LoRA (merged)

This repository contains LoRA-finetuned and merged weights based on
`openPangu-Embedded-7B-V1.1`. The LoRA adapters were merged into the
base model to produce full weights suitable for standard inference.

## Base Model

- Base model: `FreedomIntelligence/openPangu-Embedded-7B-V1.1`
- License: `OPENPANGU Model License Agreement v1.0` (see `LICENSE`)

## Training Data

- Private dataset (not released).

## Training Procedure

- Finetuning: LoRA using LLaMA-Factory.
- Export: merged full weights with `llamafactory-cli export`.

Example (paths are placeholders):

```bash
llamafactory-cli export \
  --model_name_or_path <base_model_dir> \
  --adapter_name_or_path <lora_adapter_dir> \
  --template default \
  --finetuning_type lora \
  --export_dir <export_dir> \
  --export_size 2 \
  --export_device cpu \
  --export_legacy_format False \
  --trust_remote_code True
```

## Evaluation

Evaluated with `lm-evaluation-harness` using vLLM on 4x RTX 4090.
Dates (UTC): 2026-01-04.

### GSM8K (5-shot)

- exact_match (strict-match): 0.6171
- exact_match (flexible-extract): 0.5777

### C-Eval (valid, 5-shot)

- acc: 0.6241
- acc_norm: 0.6241

Example command (paths are placeholders):

```bash
lm_eval --model vllm \
  --model_args "pretrained=<model_dir>,tensor_parallel_size=4,dtype=auto,gpu_memory_utilization=0.8,max_model_len=4096,enforce_eager=True,trust_remote_code=True" \
  --tasks gsm8k \
  --num_fewshot 5 \
  --batch_size auto
```

## Usage

This repo includes custom modeling code; `trust_remote_code=True` is required.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "killer66678/openpangu_7b_lora"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)
```

## Limitations and License Notes

- The openPangu license restricts use within the European Union.
- If you distribute a product or service based on this model, the
  license requires specific attribution and trademark notices.
- As with any LLM, outputs may be incorrect or biased.

## Acknowledgements

本研究的实验与计算工作依托于华为云昇腾AI云服务平台完成，特此对其提供的稳定算力支持表示感谢。