---
title: XCoder-Complexity-Scorer
canonical_url: "https://www.modelscope.cn/models/banksy235/XCoder-Complexity-Scorer"
md_url: "https://www.modelscope.cn/models/banksy235/XCoder-Complexity-Scorer.md"
repository: banksy235/XCoder-Complexity-Scorer
last_updated: 2024-09-08
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
parameters: 8.0B
tensor_type:
  - F16
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: 35
stars: 0
tags:
  - llama-factory
  - full
  - generated_from_trainer
---

# XCoder-Complexity-Scorer

> XCoder-Complexity-Scorer - banksy235 在 ModelScope 开源的模型。This model is a fine-tuned version of /cfs/hadoop-aipnlp/fudayuan02/model/llama3/Meta-Llama-3-8B-Instruct on the dataset dataset.

banksy235/XCoder-Complexity-Scorer 是 ModelScope 魔搭社区上的 8.0B 参数text-generation模型，采用 other 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: banksy235/XCoder-Complexity-Scorer
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 8.0B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: llama-factory, full, generated_from_trainer
- **Downloads**: 35
- **Stars**: 0
- **Last updated**: 2024-09-08

Source: https://www.modelscope.cn/models/banksy235/XCoder-Complexity-Scorer

---

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# code_complexity

This model is a fine-tuned version of [/cfs/hadoop-aipnlp/fudayuan02/model/llama3/Meta-Llama-3-8B-Instruct](https://huggingface.co//cfs/hadoop-aipnlp/fudayuan02/model/llama3/Meta-Llama-3-8B-Instruct) on the dataset 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.39.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2