---
title: lora_test
canonical_url: "https://www.modelscope.cn/models/OpenPipe/lora_test"
md_url: "https://www.modelscope.cn/models/OpenPipe/lora_test.md"
repository: OpenPipe/lora_test
last_updated: 2025-04-18
license: llama3.1
model_type:
  - llama
architectures:
  - LlamaForCausalLM
base_model:
  - meta-llama/Meta-Llama-3.1-8B-Instruct
base_model_relation: adapter
parameters: 21.0M
tensor_type:
  - BF16
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: 436
stars: 0
tags:
  - generated_from_trainer
---

# lora_test

> lora_test - OpenPipe 在 ModelScope 开源的模型。axolotl version: 0.4.1

OpenPipe/lora_test 是 ModelScope 魔搭社区上的 21.0M 参数机器学习模型，采用 llama3.1 许可，基于 meta-llama/Meta-Llama-3.1-8B-Instruct 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: OpenPipe/lora_test
- **License**: llama3.1
- **Parameters**: 21.0M
- **Base model**: meta-llama/Meta-Llama-3.1-8B-Instruct
- **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**: 436
- **Stars**: 0
- **Last updated**: 2025-04-18

Source: https://www.modelscope.cn/models/OpenPipe/lora_test

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
base_model_config: meta-llama/Meta-Llama-3.1-8B-Instruct
bf16: true
dataset_processes: 8
datasets:
- path: /tmp/train.jsonl
  type:
    field_instruction: input
    field_output: output
    field_system: system
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_prompt: ''
flash_attention: true
fp16: false
fsdp: []
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
is_llama_derived_model: true
learning_rate: 0.0002
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_r: 8
lora_target_linear: true
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
lr_scheduler: cosine
micro_batch_size: 2
model_type: LlamaForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /models/loras2/40a0412a-574f-442e-8a35-32dd97008a01
pad_to_sequence_len: true
sample_packing: true
save_safetensors: true
save_strategy: 'no'
sequence_len: 8192
special_tokens:
  eos_token: <|eot_id|>
  pad_token: <|end_of_text|>
strict: true
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0
wandb_project: OP Method
wandb_run_id: auth-12-1-24-gpt4o-relabeled-llama31
warmup_steps: 10
weight_decay: 0

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/openpipe-team/OP%20Method/runs/auth-12-1-24-gpt4o-relabeled-llama31)
# models/loras2/40a0412a-574f-442e-8a35-32dd97008a01

This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the None dataset.

## 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: 1

### Training results



### Framework versions

- PEFT 0.11.1
- Transformers 4.43.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1