---
title: sample-lora-pii-redaction
canonical_url: "https://www.modelscope.cn/models/OpenPipe/sample-lora-pii-redaction"
md_url: "https://www.modelscope.cn/models/OpenPipe/sample-lora-pii-redaction.md"
repository: OpenPipe/sample-lora-pii-redaction
last_updated: 2025-03-11
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - mistral
architectures:
  - MistralForCausalLM
parameters: 83.9M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2337
stars: 0
tags:
  - generated_from_trainer
---

# sample-lora-pii-redaction

> sample-lora-pii-redaction - OpenPipe 在 ModelScope 开源的模型。models/loras/OpenPipe/ft-development-0b0f52d6-bc53-4443-bbad-4a6103c95501-pii-7b-optimized

OpenPipe/sample-lora-pii-redaction 是 ModelScope 魔搭社区上的 83.9M 参数text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: OpenPipe/sample-lora-pii-redaction
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 83.9M
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: generated_from_trainer
- **Downloads**: 2337
- **Stars**: 0
- **Last updated**: 2025-03-11

Source: https://www.modelscope.cn/models/OpenPipe/sample-lora-pii-redaction

---

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# models/loras/OpenPipe/ft-development-0b0f52d6-bc53-4443-bbad-4a6103c95501-pii-7b-optimized

This model is a fine-tuned version of [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0174

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.42          | 0.02  | 1    | 0.3989          |
| 0.0258        | 0.21  | 13   | 0.0304          |
| 0.025         | 0.43  | 26   | 0.0220          |
| 0.0146        | 0.64  | 39   | 0.0204          |
| 0.0208        | 0.85  | 52   | 0.0196          |
| 0.0136        | 1.07  | 65   | 0.0187          |
| 0.0148        | 1.28  | 78   | 0.0181          |
| 0.0178        | 1.49  | 91   | 0.0180          |
| 0.0204        | 1.7   | 104  | 0.0175          |
| 0.0128        | 1.92  | 117  | 0.0174          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1