Update README.md

README.md

@@ -1,20 +1,43 @@
 ---
 library_name: peft
+datasets:
+- FinchResearch/TexTrend-llama2
+language:
+- en
+- de
+- es
+metrics:
+- accuracy
+tags:
+- language
+- llama
+- question-answering
+- instruct
 ---
-## Training procedure
+# Model Card: Seal
 
+## Overview
+The "Seal" model is a novel language model built on top of Meta's LLAMA-2 architecture. This model has undergone a unique training process, combining fine-tuning techniques, model weight merging, and the application of adapters, resulting in an innovative adaptation while retaining learned information from fine-tuned models. The "Seal" model's development was made possible through the incorporation of the Open Platypus methodology, which played a critical role in its creation.
 
-The following `bitsandbytes` quantization config was used during training:
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float16
-### Framework versions
+## Model Details
+- Model Name: Seal
+- Architecture: Meta's LLAMA-2
+- Training Approach: Fine-tuning with the LORA framework, model weight merging, adapter-based adaptation
+- Development Methodology: Open Platypus
+- Contributors: Your Name and Your Team
 
+## Training Process
+The "Seal" model was trained through a multi-stage process aimed at maximizing its performance and adaptability:
+1. **Fine-Tuning:** The base model (Meta's LLAMA-2) was fine-tuned using the TextTrend Corpus dataset. This initial phase helped the model learn language patterns and semantic understanding from diverse real-time text data.
+2. **Model Weight Merging:** We merged the fine-tuned model weights with pre-trained adapters, effectively integrating the knowledge acquired during fine-tuning with the broader linguistic context of the adapters.
+3. **Adapter-Based Adaptation:** Adapters were utilized to modify and enhance specific linguistic capabilities without losing the knowledge gained from the fine-tuned model. This approach allowed for targeted improvements while maintaining the general language understanding.
 
-- PEFT 0.5.0.dev0
+## Usage and Applications
+The "Seal" model is designed to excel in various natural language processing tasks, including text generation, sentiment analysis, named entity recognition, and more. Its unique training process and incorporation of the Open Platypus methodology make it particularly well-suited for tasks that require a blend of real-time language trends and established linguistic patterns.
+
+## Limitations
+- While the "Seal" model demonstrates enhanced linguistic capabilities, it may still exhibit biases or limitations present in the training data.
+- The effectiveness of the model may vary depending on the specific task and data distribution.
+
+## License
+The "Seal" model is released under a permissive license, encouraging its widespread use and experimentation. Refer to the accompanying license documentation for specific details.