---
title: b2_code_length_gpt4omini_1k
canonical_url: "https://www.modelscope.cn/models/mlfoundations-dev/b2_code_length_gpt4omini_1k"
md_url: "https://www.modelscope.cn/models/mlfoundations-dev/b2_code_length_gpt4omini_1k.md"
repository: mlfoundations-dev/b2_code_length_gpt4omini_1k
last_updated: 2025-09-28
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - Qwen/Qwen2.5-7B-Instruct
base_model_relation: finetune
parameters: 7.6B
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: 38
stars: 0
tags:
  - llama-factory
  - full
  - generated_from_trainer
---

# b2_code_length_gpt4omini_1k

> b2_code_length_gpt4omini_1k - mlfoundations-dev 在 ModelScope 开源的模型。b2codelengthgpt4omini1k

mlfoundations-dev/b2_code_length_gpt4omini_1k 是 ModelScope 魔搭社区上的 7.6B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen2.5-7B-Instruct 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlfoundations-dev/b2_code_length_gpt4omini_1k
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 7.6B
- **Base model**: Qwen/Qwen2.5-7B-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**: llama-factory, full, generated_from_trainer
- **Downloads**: 38
- **Stars**: 0
- **Last updated**: 2025-09-28

Source: https://www.modelscope.cn/models/mlfoundations-dev/b2_code_length_gpt4omini_1k

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# b2_code_length_gpt4omini_1k

This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_code_length_gpt4omini_1k 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0

### Training results



### Framework versions

- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3