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Model: singtan/my-llm-finetuned-pdf
Source: Original Platform

README.md

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+---
+license: apache-2.0
+library_name: transformers
+base_model: gpt2
+tags:
+- text-generation
+- pdf-extraction
+- fine-tuned
+datasets:
+- custom
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Fine-tuned Language Model (PDF Optimized)
+
+This model is a specialized version of **gpt2**, fine-tuned for high-context coherence based on technical documentation extracted from PDF sources.
+
+## Model Summary
+This model was developed to improve text generation accuracy and contextual understanding for specific domains covered in the provided PDF assets. It leverages the underlying architecture of `gpt2` and is optimized for the structure and vocabulary found in document-based datasets.
+
+### Key Specifications
+| Attribute | Value |
+| :--- | :--- |
+| **Base Architecture** | gpt2 |
+| **Format** | PyTorch (Transformers) |
+| **Task** | Causal Language Modeling |
+| **Language** | English (en) |
+
+## Training Configuration
+The model was fine-tuned using the following high-level hyperparameters to ensure stability and convergence:
+
+- **Epochs:** 3
+- **Batch Size:** 1
+- **Learning Rate:** 5e-05
+- **Optimized for:** Cross-Entropy Loss
+- **Hardware:** cuda
+
+### Performance Metrics
+- **Total Training Loss:** 3.680997848510742
+- **Training Runtime:** 3.6704 seconds
+
+## Data Preprocessing
+The training data was sourced from `dummy.pdf`. The pipeline included:
+1.  **Extraction:** Text recovery using `pypdf`.
+2.  **Normalization:** Regex-based whitespace cleaning and token normalization.
+3.  **Tokenization:** Model-specific subword tokenization with a max sequence length of 512 tokens.
+
+## Usage Instructions
+To utilize this model for inference, use the following snippet:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+checkpoint = "singtan/my-llm-finetuned-pdf"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+
+prompt = "Insert your context here"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=150)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Contact
+Developed by **Bibek** - Senior AI Engineering Portfolio.