Update README.md

README.md

@@ -1,62 +1,73 @@
 ---
 base_model: Qwen/Qwen2.5-3B-Instruct
 library_name: peft
-model_name: aether_2_3_model
+pipeline_tag: text-generation
 tags:
 - base_model:adapter:Qwen/Qwen2.5-3B-Instruct
 - lora
 - sft
 - transformers
 - trl
-licence: license
-pipeline_tag: text-generation
+- german
+- english
+- aether
+license: apache-2.0
+language:
+- de
+- en
 ---
 
-# Model Card for aether_2_3_model
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# Aether 2.3
 
-## Quick start
-
-```python
-from transformers import pipeline
+Aether 2.3 represents a major milestone in the Aether model series, scaling up to the **Qwen2.5-3B-Instruct** base architecture. Trained with SFT (Supervised Fine-Tuning) via Hugging Face TRL and PEFT (LoRA) on a custom 3 GB dataset using local NVIDIA RTX GPU acceleration, Aether 2.3 offers significantly higher intelligence, broader contextual understanding, and superior multilingual responses in German and English.
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+## Model Details
 
-## Training procedure
+### Model Description
 
- 
+- **Developed by:** Maxilicious20
+- **Model type:** Causal Language Model (LoRA Adapter)
+- **Language(s) (NLP):** German, English
+- **License:** Apache-2.0
+- **Finetuned from model:** Qwen/Qwen2.5-3B-Instruct
 
+## Uses
 
+### Direct Use
 
-This model was trained with SFT.
+Aether 2.3 is designed for high-capability conversational AI, complex instruction following, creative text generation, and technical reasoning. Thanks to its LoRA adapter implementation, it delivers flagship 3B-class performance while remaining light enough to run efficiently on local hardware.
 
-### Framework versions
+### How to Get Started with the Model
 
-- PEFT 0.20.0
-- TRL: 1.9.2
-- Transformers: 5.14.1
-- Pytorch: 2.13.0+cu126
-- Datasets: 5.0.1
-- Tokenizers: 0.22.2
+Use the following Python code to load Aether 2.3 with `transformers` and `peft`:
 
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
-}
-```
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model_id = "Qwen/Qwen2.5-3B-Instruct"
+adapter_id = "Maxilicious20/Aether-2.3"
+
+# Load Tokenizer and Base Model
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load Aether 2.3 LoRA Adapter
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+# Example Prompt
+messages = [
+    {"role": "system", "content": "You are Aether 2.3, an advanced AI assistant."},
+    {"role": "user", "content": "Hello! What improvements do you bring as a 3B model?"}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))