Update README.md

README.md

@@ -1,79 +1,83 @@
 ---
-base_model: sentence-transformers/all-MiniLM-L6-v2
-datasets:
-- s2orc
-- flax-sentence-embeddings/stackexchange_xml
-- ms_marco
-- gooaq
-- yahoo_answers_topics
-- code_search_net
-- search_qa
-- eli5
-- snli
-- multi_nli
-- wikihow
-- natural_questions
-- trivia_qa
-- embedding-data/sentence-compression
-- embedding-data/flickr30k-captions
-- embedding-data/altlex
-- embedding-data/simple-wiki
-- embedding-data/QQP
-- embedding-data/SPECTER
-- embedding-data/PAQ_pairs
-- embedding-data/WikiAnswers
 language: en
-library_name: sentence-transformers
 license: apache-2.0
-pipeline_tag: sentence-similarity
+library_name: sentence-transformers
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
-- llama-cpp
+- drilling-engineering
+datasets:
+- oil-gas-engineering-docs
+- drilling-engineering-manuals
+- well-drilling-reports
+- casing-design-guidelines
+- mud-logging-data
+- managed-pressure-drilling-reports
+- directional-drilling-studies
+pipeline_tag: sentence-similarity
 ---
 
-# tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF
-This model was converted to GGUF format from [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) using llama.cpp.
-Refer to the [original model card](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for more details on the model.
+# OGAI-Embedder
+This is a [sentence-transformers](https://www.SBERT.net) model fine-tuned specifically for **drilling engineering** applications in the oil and gas industry. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for tasks like **technical document retrieval, automated report analysis, and intelligent search** within **drilling-related datasets**.
 
-## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
 
-```bash
-brew install llama.cpp
-
-```
-Invoke the llama.cpp server or the CLI.
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
-### CLI:
 ```bash
-llama-cli --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -p "The meaning to life and the universe is"
+pip install -U sentence-transformers
 ```
 
-### Server:
-```bash
-llama-server --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -c 2048
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["What is the optimal mud weight for a high-angle well?", "How does managed pressure drilling improve well control?"]
+
+model = SentenceTransformer('OGAI-Embedder')
+embeddings = model.encode(sentences)
+print(embeddings)
 ```
 
-Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+## Drilling-Specific Search and Retrieval
+OGAI-Embedder can be used in **document search engines** for drilling operations, enabling semantic search across:
+- Well drilling reports
+- Casing design manuals
+- Mud logging data
+- Directional drilling surveys
+- Equipment specifications
+- Well control procedures
 
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/llama.cpp
-```
+## Training Data for Drilling Engineering
+The model has been fine-tuned using a **curated dataset** of drilling engineering documents, manuals, and field reports.
 
-Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-```
-cd llama.cpp && LLAMA_CURL=1 make
-```
+### Key Datasets Used:
+| Dataset | Description |
+|---------|------------|
+| Well Drilling Reports | Real-world drilling reports from operators |
+| Casing Design Guidelines | Technical best practices for casing design | 
+| Mud Logging Data | Drilling fluid parameters and performance records | 
 
-Step 3: Run inference through the main binary.
-```
-./llama-cli --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -p "The meaning to life and the universe is"
-```
-or 
+## Deployment for AI-Powered Drilling Engineering Assistance
+OGAI-Embedder is designed for **real-time AI integration** into oil and gas platforms. It enables:
+- **Automated report analysis** for drilling engineers.
+- **Intelligent document retrieval** from large drilling knowledge bases.
+- **Context-aware AI assistants** for well planning and execution.
+- **Enhanced decision-making** based on historical well performance data.
+
+## Model Deployment
+This model can be used with `llama.cpp` for efficient inference in drilling engineering applications.
+
+```bash
+brew install llama.cpp
+llama-cli --hf-repo OGAI-Embedder --hf-file ogai-embedder-q5_0.gguf -p "What are the key challenges in managed pressure drilling?"
 ```
-./llama-server --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -c 2048
+
+To run a server:
+```bash
+llama-server --hf-repo OGAI-Embedder --hf-file ogai-embedder-q5_0.gguf -c 2048
 ```
+
+This model is available on Hugging Face for research and commercial use under the Apache 2.0 license.
+