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Model: Alamori/GoldenNet-Qwen2.5-0.5B-Full-v1 Source: Original Platform
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134
README.md
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README.md
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---
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license: apache-2.0
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language:
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- ar
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- en
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- qwen2
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- arabic
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- iraqi
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- government
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- classification
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- ner
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- fine-tuned
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- full-finetune
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- goldennet
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datasets:
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- custom
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pipeline_tag: text-generation
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library_name: transformers
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model-index:
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- name: GoldenNet-Qwen2.5-0.5B-Full-v1
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results:
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- task:
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type: text-classification
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name: Document Classification
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metrics:
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- type: loss
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value: 0.3636
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name: Eval Loss
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---
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# GoldenNet-Qwen2.5-0.5B-Full-v1
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<div align="center">
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<img src="https://img.shields.io/badge/Golden%20Net%20AI-Iraqi%20Gov%20NLP-gold?style=for-the-badge" alt="Golden Net AI"/>
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<img src="https://img.shields.io/badge/Method-Full%20Fine--tune-red?style=for-the-badge" alt="Full"/>
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<img src="https://img.shields.io/badge/Language-Arabic-green?style=for-the-badge" alt="Arabic"/>
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</div>
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## Model Description
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**GoldenNet-Qwen2.5-0.5B-Full-v1** is a fully fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) specialized for **Iraqi Government Correspondence Processing**.
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This is the **full fine-tuning** variant where all 494M parameters were trained, potentially offering the best task-specific performance.
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### Tasks
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1. **Document Classification** - 8 categories (طلب، شكوى، تقرير، إعلام، استفسار، دعوة، تعميم، إحالة)
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2. **Named Entity Recognition** - Extracts persons, organizations, locations, dates, monetary values, laws
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## Model Comparison
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| Model | Method | Train Loss | Eval Loss | Training Time | Size |
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|-------|--------|------------|-----------|---------------|------|
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| [QLoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1) | 4-bit QLoRA | 0.448 | **0.2998** | 49s | 943MB |
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| [LoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1) | Standard LoRA | 0.496 | 0.3665 | 70s | 943MB |
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| **Full-v1** | Full Fine-tune | **0.461** | 0.3636 | 121s | 1.9GB |
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## Training Details
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| Parameter | Value |
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|-----------|-------|
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| Base Model | Qwen/Qwen2.5-0.5B-Instruct |
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| Fine-tuning Method | Full (all parameters) |
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| Learning Rate | 5e-5 |
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| Optimizer | AdamW 8-bit |
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| Epochs | 3 |
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| Batch Size | 1 (effective: 16) |
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| Max Sequence Length | 1024 |
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| Precision | BF16 |
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| Trainable Parameters | 494M (100%) |
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| Hardware | NVIDIA RTX 5070 (8GB VRAM) |
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### Loss Progression
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- Epoch 1: 0.983
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- Epoch 2: 0.328
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- Epoch 3: 0.171 (lowest among all variants!)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"Alamori/GoldenNet-Qwen2.5-0.5B-Full-v1",
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device_map="auto",
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torch_dtype="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("Alamori/GoldenNet-Qwen2.5-0.5B-Full-v1")
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# Classification example
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correspondence = """جمهورية العراق
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وزارة التربية
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العدد: 1234/ت/2025
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إلى/ السيد مدير عام التعليم المحترم
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م/ طلب تعيين معلمين
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نرجو الموافقة على تعيين 50 معلماً.
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مع التقدير"""
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instruction = "صنّف المراسلة الحكومية التالية إلى إحدى الفئات: طلب، شكوى، تقرير، إعلام، استفسار، دعوة، تعميم، إحالة. أجب بصيغة JSON."
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messages = [{"role": "user", "content": f"{instruction}\n\n{correspondence}"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
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print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
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```
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## When to Use This Model
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- **Use Full-v1** when you need maximum task-specific performance and have sufficient storage/memory
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- **Use QLoRA-v1** for best balance of quality and efficiency (recommended for most cases)
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- **Use LoRA-v1** for comparison or when you need standard LoRA compatibility
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## Related Models
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- [GoldenNet-Qwen2.5-0.5B-QLoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1) - 4-bit quantized (best eval loss)
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- [GoldenNet-Qwen2.5-0.5B-LoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1) - Standard LoRA
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## License
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||||
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Apache 2.0
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||||
---
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<div align="center">
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<b>Developed by Golden Net AI</b><br>
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<i>Empowering Iraqi Government Digital Transformation</i>
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</div>
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added_tokens.json
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all_results.json
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all_results.json
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{
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"epoch": 3.0,
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"eval_loss": 0.36356300115585327,
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"eval_runtime": 0.7586,
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"eval_samples_per_second": 26.364,
|
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"eval_steps_per_second": 26.364,
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"total_flos": 359524401020928.0,
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"train_loss": 0.46062264027017535,
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"train_runtime": 120.7529,
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"train_samples_per_second": 4.298,
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"train_steps_per_second": 0.273
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}
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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||||
{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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||||
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"dtype": "float32",
|
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 896,
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"initializer_range": 0.02,
|
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"intermediate_size": 4864,
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention"
|
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],
|
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"max_position_embeddings": 32768,
|
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"max_window_layers": 21,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 14,
|
||||
"num_hidden_layers": 24,
|
||||
"num_key_value_heads": 2,
|
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"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
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"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
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"transformers_version": "4.57.1",
|
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"use_cache": false,
|
||||
"use_sliding_window": false,
|
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"vocab_size": 151936
|
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}
|
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eval_results.json
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eval_results.json
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{
|
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"epoch": 3.0,
|
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"eval_loss": 0.36356300115585327,
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"eval_runtime": 0.7586,
|
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"eval_samples_per_second": 26.364,
|
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"eval_steps_per_second": 26.364
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}
|
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generation_config.json
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generation_config.json
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{
|
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
|
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"temperature": 0.7,
|
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"top_k": 20,
|
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"top_p": 0.8,
|
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"transformers_version": "4.57.1"
|
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}
|
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151388
merges.txt
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151388
merges.txt
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3
model.safetensors
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model.safetensors
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size 1976163472
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special_tokens_map.json
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special_tokens_map.json
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{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
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"<|object_ref_start|>",
|
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"<|object_ref_end|>",
|
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"<|box_start|>",
|
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"<|box_end|>",
|
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"<|quad_start|>",
|
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"<|quad_end|>",
|
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"<|vision_start|>",
|
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"<|vision_end|>",
|
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"<|vision_pad|>",
|
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"<|image_pad|>",
|
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"<|video_pad|>"
|
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],
|
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"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false
|
||||
}
|
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}
|
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tokenizer.json
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tokenizer.json
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|
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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size 11421896
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208
tokenizer_config.json
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tokenizer_config.json
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{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
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"added_tokens_decoder": {
|
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"151643": {
|
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"content": "<|endoftext|>",
|
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"lstrip": false,
|
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"normalized": false,
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8
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Normal file
8
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4
trainer_log.jsonl
Normal file
4
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Normal file
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64
trainer_state.json
Normal file
64
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3
training_args.bin
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3
training_args.bin
Normal file
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BIN
training_loss.png
Normal file
BIN
training_loss.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 36 KiB |
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user