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Model: chanceQZhang/zhihu-tech-zhichang-qwen-7b
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---
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. -->
[<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: `0.13.0.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

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---
base_model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- axolotl
- base_model:adapter:Orion-zhen/Meissa-Qwen2.5-7B-Instruct
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.18.1

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---
base_model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- axolotl
- base_model:adapter:Orion-zhen/Meissa-Qwen2.5-7B-Instruct
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.18.1

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{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system
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config.json Normal file
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{
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[2026-01-13 12:24:55,936] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:4815] baseline 0.000GB ()
[2026-01-13 12:24:55,937] [INFO] [axolotl.cli.config.load_cfg:259] [PID:4815] config:
{
"activation_offloading": false,
"adapter": "lora",
"axolotl_config_path": "zhihu_sft.yml",
"base_model": "Orion-zhen/Meissa-Qwen2.5-7B-Instruct",
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],
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[2026-01-13 12:24:55,938] [INFO] [axolotl.cli.utils.load.load_model_and_tokenizer:40] [PID:4815] loading tokenizer... Orion-zhen/Meissa-Qwen2.5-7B-Instruct
[2026-01-13 12:24:56,619] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:285] [PID:4815] EOS: 151645 / <|im_end|>
[2026-01-13 12:24:56,619] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:286] [PID:4815] BOS: None / None
[2026-01-13 12:24:56,619] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:287] [PID:4815] PAD: 151643 / <|endoftext|>
[2026-01-13 12:24:56,619] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:288] [PID:4815] UNK: None / None
[2026-01-13 12:24:56,619] [INFO] [axolotl.cli.utils.load.load_model_and_tokenizer:43] [PID:4815] loading model...
[2026-01-13 12:24:56,739] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:87] [PID:4815] Patched Trainer.evaluation_loop with nanmean loss calculation
[2026-01-13 12:24:56,740] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:138] [PID:4815] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
[2026-01-13 12:24:56,741] [INFO] [axolotl.loaders.patch_manager._apply_multipack_patches:344] [PID:4815] Applying multipack dataloader patch for sample packing...
Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards: 25%|███████████████████████▎ | 1/4 [00:00<00:02, 1.21it/s]
Loading checkpoint shards: 50%|██████████████████████████████████████████████▌ | 2/4 [00:01<00:01, 1.21it/s]
Loading checkpoint shards: 75%|█████████████████████████████████████████████████████████████████████▊ | 3/4 [00:02<00:00, 1.29it/s]
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00, 1.85it/s]
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00, 1.57it/s]

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merged/added_tokens.json Normal file
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}

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{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system
' + system_message + '<|im_end|>
' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user
' + content + '<|im_end|>
<|im_start|>assistant
' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '
' }}{% endif %}{% endfor %}

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merged/config.json Normal file
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{
"architectures": [
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},
"151654": {
"content": "<|vision_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

1
vocab.json Normal file

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