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Model: maftuh-main/batik-qwen1.5b-merged Source: Original Platform
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README.md
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---
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library_name: transformers
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- batik
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- indonesia
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- budaya
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- qwen2
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- lora
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- qlora
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- conversational
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language:
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- id
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pipeline_tag: text-generation
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---
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# Wastra.ai — Batik Qwen 1.5B (Merged)
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Model bahasa hasil fine-tuning dari **Qwen2.5-1.5B-Instruct**, dilatih khusus sebagai asisten pakar budaya **Batik Nusantara**. Model ini merupakan hasil merge antara base model dengan adapter LoRA yang dilatih menggunakan teknik QLoRA (4-bit quantization).
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## Model Description
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Wastra.ai adalah model percakapan yang berperan sebagai pakar budaya batik Indonesia, dirancang untuk menjawab pertanyaan seputar:
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- Sejarah dan filosofi batik
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- Teknik dan proses pembuatan batik (mori, malam, pencelupan, dsb.)
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- Ragam motif batik dari berbagai daerah
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- Status UNESCO dan pelestarian batik sebagai warisan budaya
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Model ini menjawab dengan gaya bahasa **formal, informatif, edukatif**, dan menolak secara elegan pertanyaan di luar topik batik.
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- **Developed by:** Muhammad Maftuh
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- **Model type:** Causal Language Model (fine-tuned, merged LoRA)
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- **Language(s):** Bahasa Indonesia
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- **License:** Apache 2.0 (mengikuti lisensi base model)
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- **Finetuned from model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
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## Uses
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### Direct Use
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Model ini dapat digunakan langsung sebagai chatbot edukatif untuk topik batik — cocok untuk aplikasi pembelajaran, chatbot museum digital, atau fitur tanya-jawab dalam aplikasi budaya seperti **BatikLens**.
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### Out-of-Scope Use
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Model ini **tidak dirancang** untuk menjawab pertanyaan di luar topik batik dan budaya terkait. Untuk kebutuhan umum, gunakan model dasar Qwen2.5-1.5B-Instruct.
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## How to Get Started with the Model
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "maftuh-main/batik-qwen1.5b-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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messages = [
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{"role": "system", "content": "Kamu adalah Wastra.ai, sebuah kecerdasan buatan yang berperan sebagai Pakar Budaya Batik Nusantara yang sangat berwibawa."},
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{"role": "user", "content": "Jelaskan filosofi motif Parang."}
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training Details
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### Training Data
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Dataset percakapan sintetis dan kurasi seputar batik (`maftuh-main/dataset-batik-trl-sft`), mencakup topik sejarah, teknik pembuatan, filosofi motif, dan pengakuan UNESCO terhadap batik Indonesia.
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### Training Procedure
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- **Base model:** Qwen2.5-1.5B-Instruct
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- **Metode:** QLoRA (4-bit NF4 quantization) + LoRA adapter, kemudian di-merge ke base model
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- **LoRA config:** r=16, alpha=32, dropout=0.05, target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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- **Training regime:** bf16 mixed precision
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- **Optimizer:** paged_adamw_8bit
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- **Learning rate:** 1e-4, cosine scheduler, 100 warmup steps
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- **Max sequence length:** 512
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- **Hardware:** Kaggle Notebook — NVIDIA Tesla T4 x2
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## Limitations
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- Model dibatasi hanya untuk topik batik; performa di luar domain ini belum diuji.
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- Sebagai model 1.5B parameter, kapabilitas reasoning kompleks lebih terbatas dibanding model yang lebih besar.
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- Dataset training bersifat sintetis/terkurasi sehingga kualitas jawaban bergantung pada cakupan dataset.
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## Model Card Contact
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Muhammad Maftuh — proyek [BatikLens](https://huggingface.co/maftuh-main)
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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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61
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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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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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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"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,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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151645,
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151643
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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": "5.13.1"
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}
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model.safetensors
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size 3087467144
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|object_ref_start|>",
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"<|box_start|>",
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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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"is_local": false,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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