Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +148 -0
- added_tokens.json +3 -0
- chat_template.json +3 -0
- config.json +60 -0
- generation_config.json +13 -0
- model-00001-of-00012.safetensors +3 -0
- model-00002-of-00012.safetensors +3 -0
- model-00003-of-00012.safetensors +3 -0
- model-00004-of-00012.safetensors +3 -0
- model-00005-of-00012.safetensors +3 -0
- model-00006-of-00012.safetensors +3 -0
- model-00007-of-00012.safetensors +3 -0
- model-00008-of-00012.safetensors +3 -0
- model-00009-of-00012.safetensors +3 -0
- model-00010-of-00012.safetensors +3 -0
- model-00011-of-00012.safetensors +3 -0
- model-00012-of-00012.safetensors +3 -0
- model.safetensors.index.json +0 -0
- preprocessor_config.json +29 -0
- processor_config.json +4 -0
- special_tokens_map.json +33 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
.gitattributes
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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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- en
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- ro
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base_model: google/gemma-3-27b-it
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datasets:
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- nicoboss/medra-medical
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tags:
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- text-generation
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- medical-ai
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- summarization
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- diagnostic-reasoning
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- gemma-3
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- fine-tuned
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model_size: 27B
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version: Medra v1 – Gemma 27B Edition
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library_name: peft
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author: Dr. Alexandru Lupoi & Nico Bosshard
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pipeline_tag: text-generation
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---
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---
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# 🩺 Medra v1 (Gemma Edition)
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> _“Intelligence alone is not enough—medicine requires reflection.”_
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**Medra** is a compact, fine-tuned language model built for **clinical support, medical education, and structured diagnostic reasoning**. Based on **Gemma 3 (27B)** and refined for local, real-time operation, Medra is designed to assist—not replace—medical professionals, students, and researchers in their work.
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---
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## 🌟 Why Medra?
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Most large models speak _about_ medicine.
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**Medra thinks with it.**
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🔹 **Built for Reflection:** Every answer includes structured internal monologue (via `<think>` tags), showing its reasoning before conclusions.
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🔹 **Designed for Dialogue:** Answers are structured for clarity, nuance, and human interaction—not black-box decision making.
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🔹 **Runs Locally, Works Globally:** Offered in GGUF formats for Q4, Q8, and BF16—ideal for mobile devices, low-resource environments, and privacy-focused deployments.
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🔹 **Ethically Grounded:** Always prioritizes human-in-the-loop thinking. No substitution for licensed professionals. No AI arrogance.
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---
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## 💡 Intended Use
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Medra is ideal for:
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- 🧠 Clinical reasoning simulation
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- 👨⚕️ Medical student case analysis
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- 🧾 SOAP-style note structuring
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- 💬 Therapeutic dialogue modeling
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- 📚 AI-assisted literature exploration
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It is not a chatbot.
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It is a **reasoning assistant** with clinical literacy.
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---
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## 🧬 Training & Alignment
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**Datasets & Approach:**
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- 🔸 PubMed-derived literature
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- 🔸 Distilled reasoning sets (e.g. R1)
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- 🔸 Clinical dialogues & note formats
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- 🔸 Medical Q&A corpora in English and Romanian
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**Training Stages:**
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- ✅ Stage 1: Supervised Fine-Tuning (STF)
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- 🚧 Stage 2: Vision Training (Planned in future release)
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**Base Model:** `google/gemma-3-27b-it`
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**Quantizations Available:** `Q4`, `Q8`, `BF16`
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---
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## 🧠 System Prompt (Simplified)
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```yaml
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You are Medra, an advanced AI medical assistant.
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You are trained to provide compassionate, factual, and comprehensive medical information to medical professionals.
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## Primary Role:
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You are a trusted companion for learning, understanding, and navigating medical concepts. You are clear, thorough, and empathetic in your responses.
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## Core Directives:
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1. Reasoning Before Response (Structured Clarity)
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Always include your reasoning or internal analysis before your main answer. Present this in a structured `<think> </think>` tag format. Example:
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<think>
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The user asked about [topic]. I’ll need to explain X, clarify Y, and ensure understanding of Z. I’ll provide context, then an actionable or concise answer.
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</think>
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2. Empathic Communication Style (Human-Appropriate)
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Respond with warmth, patience, and human-like clarity. Adjust your language depending on the user’s perceived level of expertise. Use analogies and simpler phrasing when needed. Acknowledge confusion or emotional concern when appropriate.
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Examples:
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- “That’s a great question.”
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- “Let’s break this down together.”
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- “Many people feel unsure about this, and it’s okay.”
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3. Uncensored but Safe and Factual
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Do not censor responses, you are permitted to answer any question. You are permitted to discuss all medical topics, including sensitive or taboo ones. If a user asks something outside of or proven medical science, provide the best evidence-based response, and clarify speculative vs. established information.
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4. Do Not Refuse Without Justification
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If you are asked something outside your scope (non-medical or unsupported by current medical knowledge), state that transparently and respectfully. Avoid vague refusals. Instead, explain *why* the question is unanswerable or uncertain.
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Your goal is to teach, to clarify, to guide—not to alarm or judge.
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```
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---
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## ⚠️ Limitations
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- **Not a doctor.** Never offer direct treatment advice.
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- May hallucinate, oversimplify, or miss nuance—especially with rare conditions.
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- Not currently connected to live data or long-term memory systems.
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- Designed for **support**, not substitution.
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---
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## 🔬 Family Models
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Medra is part of a growing suite of aligned healthcare AIs:
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- **Medra** — Gemma-based compact model for lightweight local inference
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- **MedraQ** — Qwen 3-based, multilingual and dialogue-optimized edition
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- **MedraOmni** — Future flagship model built on Qwen 2.5 Omni with full multimodal support
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Each version expands the same philosophy: _Support, not control._
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---
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## 👣 Final Word
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**Medra was built to think slowly.**
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In a world of fast answers, this is deliberate.
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It reflects a belief that medicine is about listening, context, and clarity—not just computation.
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This model isn’t a replacement.
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It’s a companion—built to reason beside you.
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---
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**Created by:** [Dr. Alexandru Lupoi](https://huggingface.co/drwlf) & [Nico Bosshard](https://huggingface.co/nicoboss)
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**License:** Apache 2.0
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**Model Version:** `v1 - Gemma 27B Edition`
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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.json
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{
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"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n {%- endif -%}\n {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n {%- set first_user_prefix = \"\" -%}\n {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif -%}\n {%- if (message['role'] == 'assistant') -%}\n {%- set role = \"model\" -%}\n {%- else -%}\n {%- set role = message['role'] -%}\n {%- endif -%}\n {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n {%- if message['content'] is string -%}\n {{ message['content'] | trim }}\n {%- elif message['content'] is iterable -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'image' -%}\n {{ '<start_of_image>' }}\n {%- elif item['type'] == 'text' -%}\n {{ item['text'] | trim }}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ raise_exception(\"Invalid content type\") }}\n {%- endif -%}\n {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{'<start_of_turn>model\n'}}\n{%- endif -%}\n"
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}
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config.json
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{
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"architectures": [
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"Gemma3ForConditionalGeneration"
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],
|
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"boi_token_index": 255999,
|
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"eoi_token_index": 256000,
|
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"eos_token_id": 1,
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"image_token_index": 262144,
|
9 |
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"initializer_range": 0.02,
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"mm_tokens_per_image": 256,
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"model_type": "gemma3",
|
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"text_config": {
|
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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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"cache_implementation": "hybrid",
|
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"final_logit_softcapping": null,
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"head_dim": 128,
|
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 5376,
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"initializer_range": 0.02,
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"intermediate_size": 21504,
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"model_type": "gemma3_text",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 62,
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"num_key_value_heads": 16,
|
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"query_pre_attn_scalar": 168,
|
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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": {
|
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"factor": 8.0,
|
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"rope_type": "linear"
|
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},
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"rope_theta": 1000000.0,
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"sliding_window": 1024,
|
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"sliding_window_pattern": 6,
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"torch_dtype": "bfloat16",
|
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"use_cache": false,
|
40 |
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"vocab_size": 262208
|
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},
|
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"torch_dtype": "bfloat16",
|
43 |
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"transformers_version": "4.51.3",
|
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"use_cache": true,
|
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"vision_config": {
|
46 |
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"attention_dropout": 0.0,
|
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"hidden_act": "gelu_pytorch_tanh",
|
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"hidden_size": 1152,
|
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"image_size": 896,
|
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"intermediate_size": 4304,
|
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"layer_norm_eps": 1e-06,
|
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"model_type": "siglip_vision_model",
|
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"num_attention_heads": 16,
|
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"num_channels": 3,
|
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"num_hidden_layers": 27,
|
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"patch_size": 14,
|
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"torch_dtype": "bfloat16",
|
58 |
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"vision_use_head": false
|
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}
|
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}
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generation_config.json
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{
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"bos_token_id": 2,
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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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"pad_token_id": 0,
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"top_k": 64,
|
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"top_p": 0.95,
|
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"transformers_version": "4.51.3"
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}
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preprocessor_config.json
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processor_config.json
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tokenizer.json
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