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Model: solvrays/solvrays-finetuned-pdf
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: google/gemma-2b-it
+language: en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- precision-grounding
+- document-qa
+- zero-hallucination
+- legal-tech
+- technical-analysis
+---
+
+# 📂 Solvrays Finetuned Pdf - Document AI
+
+## 🌟 Model Overview
+This model is a high-precision fine-tuning of **google/gemma-2b-it**, specifically architected for **Zero-Hallucination Technical Retrieval**. It has been trained on a proprietary dataset of technical and architectural documentation to ensure deep contextual grounding.
+
+### 🚀 Key Capabilities
+- **Technical Grounding**: Prioritizes factual documentation over generative speculation.
+- **Chunk-Aware Memory**: Optimized for overlapping document segments (256-token window).
+- **Deterministic Precision**: Best used with `do_sample=False` for architectural accuracy.
+
+## 💻 Professional Implementation
+The model requires specific prompt construction to trigger its 'Knowledge Retrieval' mode:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = 'solvrays/solvrays-finetuned-pdf'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    device_map='auto', 
+    torch_dtype=torch.bfloat16, 
+    quantization_config={'load_in_4bit': True}
+)
+
+def query_model(user_query):
+    # High-Precision Retrieval Template
+    prompt = f'### Knowledge Retrieval Content: {user_query}\n### Verified Response: '
+    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True).split('### Verified Response:')[-1].strip()
+```
+
+## 📊 Technical Specifications
+| Feature | Configuration |
+| :--- | :--- |
+| **Base Model** | google/gemma-2b-it |
+| **Precision** | BrainFloat16 (BF16) |
+| **Fine-tuning** | QLoRA (4-bit Normalized Float) |
+| **LoRA Rank (r)** | 16 |
+| **LoRA Alpha** | 32 |
+| **Target Modules** | q, k, v, o, gate, up, down |
+| **Training Epochs** | 25 |
+
+## 🛠 Training Environment
+- **Hardware**: NVIDIA L4 x 2 (Dual GPU Architecture)
+- **Optimizer**: Paged AdamW 8-bit
+- **Context Length**: 256 tokens per block
+
+## ⚠️ Constraints & Risk Mitigation
+- **Out-of-Scope**: This model is not intended for general conversation or creative writing. It is a specialized document analyst.
+- **Hallucination Control**: If information is not present in the internal weights, the model is trained to state 'Not Documented' or provide an empty response for verification.
+- **Numerical Accuracy**: Always cross-verify critical measurements with original PDF source material.
+
+---
+**Senior AI Architect & Developer**: Solvrays

chat_template.jinja

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+{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
+' + message['content'] | trim + '<end_of_turn>
+' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
+'}}{% endif %}

config.json

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+{
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_bidirectional_attention": null,
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "5.0.0"
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "extra_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}