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Model: sarimahsan101/Qwen2.5-0.5B-HiddenDistilled Source: Original Platform
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71
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- qwen
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- knowledge-distillation
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- hidden-state-distillation
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language:
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- en
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metrics:
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- perplexity
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pipeline_tag: text-generation
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---
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# Qwen2.5-0.5B-HiddenDistilled
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This repository contains the **fully merged base + adapter weights** for Qwen2.5-0.5B-Instruct distilled from the teacher model **Qwen2.5-3B-Instruct**. The distillation pipeline optimizes a composite objective combining supervised learning cross-entropy, logit-level KL divergence, and MSE alignment of projected hidden states.
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* **Base Student Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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* **Teacher Model:** [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
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* **LoRA Adapter Repo:** [sarimahsan101/Qwen2.5-0.5B-HiddenDistilled-LoRA](https://huggingface.co/sarimahsan101/Qwen2.5-0.5B-HiddenDistilled-LoRA)
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* **Training Code (GitHub):** [sarimahsan101/distillation-hiddenstates](https://github.com/sarimahsan/distillation-hiddenstates-code)
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## 📊 Trial Run Evaluation Metrics
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Evaluation metrics compiled on a single **NVIDIA Tesla T4 (16GB)** GPU for **1 epoch** on a subset of Dolly, Alpaca SFT, and Ultrachat:
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| Metric | Before Distillation | After Distillation | Change | Status |
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| :--- | :---: | :---: | :---: | :---: |
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| **Validation Perplexity** | 5.0924 | 5.2620 | +0.1696 | ✗ |
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| **Teacher-Student KL Divergence** | 2.7913 | 1.9637 | -0.8276 | ✓ |
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| **Hidden State Cosine Similarity** | 0.0075 | 0.0054 | -0.0021 | ✗ |
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## 🚀 How to Use (Merged Model)
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You can load this model directly using standard Hugging Face Transformers:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "sarimahsan101/Qwen2.5-0.5B-HiddenDistilled"
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# Load tokenizer and merged model
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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model.eval()
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# Inference example
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messages = [{"role": "user", "content": "Explain gravity in one sentence."}]
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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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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=50)
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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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## 🛠️ Training Configurations & Details
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* **Framework:** PyTorch & Hugging Face Transformers / Trainer
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* **Quantization:** 4-bit NF4 double quantization (`bitsandbytes`) for training, adapter weights were merged with FP16 base student.
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* **Loss Weights:** Cross-Entropy: 0.3, KL Divergence: 0.4, Hidden MSE: 0.3
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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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57
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": 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,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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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": "5.13.0",
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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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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": "5.13.0"
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}
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model.safetensors
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model.safetensors
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size 988097824
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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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_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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"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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