Update to new architecture (cleaned data, v2 pipeline)

README.md

@@ -1,109 +1,209 @@
 ---
-license: apache-2.0
-language:
-- en
-- zh
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: peft
+pipeline_tag: text-generation
 tags:
-- workflow-automation
-- process-automation
-- qwen2.5
+- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
 - lora
-- leyoai
-base_model: Qwen/Qwen2.5-1.5B-Instruct
+- sft
+- transformers
+- trl
 ---
 
-# LeyoAI Flow Automation Assistant (Small)
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
 
-English | [中文](#中文)
 
-## English
+
+## Model Details
 
 ### Model Description
-Standard workflow automation and classification assistant
-
-### Installation
-```bash
-pip install transformers peft torch
-```
-
-### Usage Scenarios
-- Workflow classification
-- Process automation
-- Task routing
-
-### How to Use
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-# Load base model
-base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
-
-# Load LoRA adapter
-model = PeftModel.from_pretrained(base_model, "FFZwai/leyoai-flow-small")
-
-# Generate response
-input_text = "Your question here"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs, max_length=512)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-### Training Details
-- **Base Model**: Qwen/Qwen2.5-1.5B-Instruct
-- **LoRA Rank**: 16
-- **Training Data**: 3,500 samples
-- **Validation Data**: 175 samples
-- **Framework**: QLoRA + SFTTrainer
 
----
+<!-- Provide a longer summary of what this model is. -->
 
-## 中文
 
-### 模型描述
-标准版流程自动化和分类助手
 
-### 安装方法
-```bash
-pip install transformers peft torch
-```
+- **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]
 
-### 使用场景
-- 工作流分类
-- 流程自动化
-- 任务路由
+### Model Sources [optional]
 
-### 使用方法
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
+<!-- Provide the basic links for the model. -->
 
-# 加载基座模型
-base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-# 加载 LoRA 适配器
-model = PeftModel.from_pretrained(base_model, "FFZwai/leyoai-flow-small")
+## Uses
 
-# 生成回复
-input_text = "你的问题"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs, max_length=512)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### 训练细节
-- **基座模型**: Qwen/Qwen2.5-1.5B-Instruct
-- **LoRA Rank**: 16
-- **训练数据**: 3,500 samples
-- **验证数据**: 175 samples
-- **训练框架**: QLoRA + SFTTrainer
+### 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]
 
-## About LeyoAI
+## Model Card Contact
 
-LeyoAI is an AI MaaS platform by 杭州市上城区乐友信息服务工作室, providing specialized AI assistants for cybersecurity, video safety, workflow automation, and data analytics.
+[More Information Needed]
+### Framework versions
 
-**Website**: https://leyoai.vercel.app
+- PEFT 0.18.1

adapter_config.json

@@ -30,9 +30,9 @@
   "revision": null,
   "target_modules": [
     "q_proj",
+    "o_proj",
     "k_proj",
-    "v_proj",
-    "o_proj"
+    "v_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

training_args.bin

@@ -1,3 +1,3 @@
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