---
library_name: peft
license: gpl-3.0
base_model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct
tags:
- axolotl
- base_model:adapter:Orion-zhen/Meissa-Qwen2.5-7B-Instruct
- lora
- transformers
datasets:
- chanceQZhang/zhihuhighvotes
pipeline_tag: text-generation
model-index:
- name: outputs/zhihu-tech-career-lora
  results: []
---

<!-- 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. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.13.0.dev0`
```yaml
# config_sft_zhihu.yml

base_model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# 使用您上传的数据集
datasets:
  - path: chanceQZhang/zhihuhighvotes
    type: chat_template  # ChatML 格式使用 chat_template
    split: train
# 提速核心
sample_packing: true
pad_to_sequence_len: true



# LoRA 配置
adapter: lora
lora_r: 8
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj
  
# --- 核心优化：显存节省配置 ---
bf16: true                    # 30/40系列或A系列显卡必开，提升速度且省显存
fp16: false
gradient_checkpointing: true  # 必开！用计算时间换空间，大幅降低显存占用
flash_attention: true         # 必开！大幅降低长文本下的显存需求

# 训练配置
sequence_len: 2048
micro_batch_size: 6
gradient_accumulation_steps: 3
num_epochs: 2
learning_rate: 0.00005
# 减少中间开销
logging_steps: 10
eval_steps: 100
save_steps: 302

# 输出
output_dir: ./outputs/zhihu-tech-career-lora
```

</details><br>

# outputs/zhihu-tech-career-lora

This model is a fine-tuned version of [Orion-zhen/Meissa-Qwen2.5-7B-Instruct](https://huggingface.co/Orion-zhen/Meissa-Qwen2.5-7B-Instruct) on the chanceQZhang/zhihuhighvotes 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: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 16
- training_steps: 536

### Training results



### Framework versions

- PEFT 0.18.1
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.2
- Tokenizers 0.22.2