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Model: Sashvat/HQQ-270M Source: Original Platform
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LICENSE
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LICENSE
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Sashvat AI License v1.0
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Permission is hereby granted to use, copy, modify, and distribute this model and derivative works for any purpose, including commercial use, provided that proper attribution is given.
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Attribution Requirement:
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Any use, redistribution, or derivative of this model must include a clear and visible attribution statement:
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"Made by Sashvat AI"
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This attribution must appear in any papers, blog posts, apps, services, or other works that use the model or its derivatives.
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Disclaimer:
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This model is provided "as is" without warranties of any kind, express or implied.
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Sashvat AI shall not be held liable for any damages arising from the use of this model.
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Metric.png
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README.md
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---
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language:
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- en
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base_model:
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- google/gemma-3-270m-it
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library_name: transformers
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tags:
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- NukeverseAi
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- HQQ
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- HQQ-270M
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- HQQ_270M
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- DeepResearch
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- gemma3
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- gpt_oss
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pipeline_tag: text-generation
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license: other
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---
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# 🚀 Introducing : HQQ-270M
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## Overview :-
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**HQQ-270M** model is developed by **Sashvat AI** by finetuning [Gemma-3](https://huggingface.co/google/gemma-3-270m-it)
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It specializes in **transforming complex, multi-layered user queries into optimized, high-quality Google search queries** .
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⚠️ **Usage Requirement :**
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All input queries **must begin with the prefix `HQQ:`** ( short for **High Quality Query** ) .
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This ensures the model knows the input is intended for query optimization .
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---
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## 🔍 What does it do?
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- Converts **Deep research prompts** into precise search queries.
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- Handles **Broad or ambiguous questions** by breaking them into focused, search-ready chunks.
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- Enhances **information retrieval** by optimizing queries for search engines.
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This model is ideal for :
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- Researchers
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- Students
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- Analysts
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- Anyone needing **faster + higher-quality search results** .
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---
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## ✨ Key Features :
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- **Fine-tuned from Gemma-3** → retains strong language reasoning .
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- **Fast & Efficient** → Gemma's architecture is designed to make the model fast & efficient .
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- **Optimized for real-world queries** → search queries are short, relevant, and actionable.
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- **Prefix-activated (`HQQ:`)** → ensures model is used for its intended purpose.
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---
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## 📦 How to Use
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### 🔧 Installation
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```bash
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pip install transformers accelerate huggingface_hub
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```
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### 🖥️ Inference
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``` python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Sashvat/HQQ-270M")
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model = AutoModelForCausalLM.from_pretrained("Sashvat/HQQ-270M")
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system_prompt = """
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Convert text after "HQQ: " into an optimized Google search query. Extract key terms, remove filler words, focus on searchable keywords.
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"""
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query = "HQQ: What are the economic, political, and environmental implications of large-scale adoption of nuclear fusion by 2050?"
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messages = [
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{"role": "system", "content": system_prompt },
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{"role": "user", "content": query }
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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```
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---
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## 🚀 Example Output :-
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**Input:**
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> `HQQ: What are the economic, political, and environmental implications of large-scale adoption of nuclear fusion by 2050?`
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**Output :**
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> `"economic political environmental implications" "large-scale adoption nuclear fusion" 2050`
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---
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### Training Loss vs Steps :-
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`Note` : **500 Steps / ~6 Epochs**
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<img src="https://huggingface.co/Sashvat/HQQ-270M/resolve/main/Metric.png">
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---
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## 📊 Intended Use :-
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This model is intended for :
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- Query optimization for **Google search and other search engines**
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- **Information retrieval pipelines** .
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- Assisting **deep research tasks** .
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⚠️ **Important :** Input must **always** begin with `HQQ:` . Without this prefix , results may be unpredictable .
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---
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## 📜 License
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This model is released under the **Sashvat AI License v1.0**.
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You may freely use, modify, and distribute this model, including for commercial purposes .
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However, any use **must clearly state** :
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**"Made by Sashvat AI"**
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📄 Full license: [LICENSE](https://huggingface.co/Sashvat/HQQ-270M/blob/main/LICENSE)
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---
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## 🏢 About Sashvat AI
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We are **Sashvat AI**, from BHARAT 🕉️ . building next-generation productivity tools, AI agents, and research accelerators .
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---
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## 📌 Citation
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If you use this model, please cite :
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```
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@misc{2025-SashvatAI-HQQ-270M,
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title = {HQQ-270M},
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author = {SashvatAI},
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year = {2025},
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url = {https://huggingface.co/Sashvat/HQQ-270M}
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}
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```
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---
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added_tokens.json
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{
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"<image_soft_token>": 262144
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}
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chat_template.jinja
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chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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config.json
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config.json
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{
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"_sliding_window_pattern": 6,
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"architectures": [
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"Gemma3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": null,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"final_logit_softcapping": null,
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 640,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"model_type": "gemma3_text",
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"num_attention_heads": 4,
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"num_hidden_layers": 18,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_local_base_freq": 10000.0,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": 512,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.55.2",
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"use_bidirectional_attention": false,
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"use_cache": true,
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"vocab_size": 262144
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}
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{
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"cache_implementation": "hybrid",
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"do_sample": true,
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"eos_token_id": [
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1,
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106
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],
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "4.55.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1ef2a8590be17daec4581c03b1b079a68e488e57f71d588df7f0c8555b61ec20
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size 536223056
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special_tokens_map.json
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{
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"boi_token": "<start_of_image>",
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"bos_token": {
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"content": "<bos>",
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"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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"eoi_token": "<end_of_image>",
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"eos_token": {
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"content": "<eos>",
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"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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"image_token": "<image_soft_token>",
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"pad_token": "<eos>",
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"unk_token": {
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"content": "<unk>",
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"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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}
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:5c3a398bbadacc43b7ef02a9b3c7b7a5f9f01e94e68fd686d5aa230024829e58
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size 33384832
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tokenizer.model
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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size 4689074
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