Update README.md

README.md

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 - unsloth
 ---
 
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-## Uses
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-### Direct Use
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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-## More Information [optional]
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-### Framework versions
+---
+license: apache-2.0
+language:
+- el
+base_model:
+- sesame/csm-1b
+pipeline_tag: text-to-speech
+---
 
-- PEFT 0.17.0
+# Description
+Welcome to Moira.AI GreekTTS, a state-of-the-art text-to-speech model fine-tuned specifically for Greek language synthesis! This model is built on the powerful sesame/csm-1b architecture, which has been fine-tuned with Greek speech data to provide high-quality, natural-sounding speech generation.
+
+Moira.AI excels in delivering lifelike, expressive speech, making it ideal for a wide range of applications, including virtual assistants, audiobooks, accessibility tools, and more. By leveraging the power of large-scale transformer-based models, Moira.AI ensures fluid prosody and accurate pronunciation of Greek text.
+
+Key Features:
+
+- Fine-tuned specifically for Greek TTS.
+- Built on the robust sesame/csm-1b model, ensuring high-quality performance.
+- Capable of generating natural-sounding, expressive Greek speech.
+- Ideal for integration into applications requiring high-quality, human-like text-to-speech synthesis in Greek.
+- Explore the model and see how it can enhance your Greek TTS applications!
+
+
+# How to use it
+https://docs.unsloth.ai/get-started/install-and-update/conda-install
+
+```
+conda create --name unsloth_env \
+    python=3.11 \
+    pytorch-cuda=12.1 \
+    pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
+    -y
+```
+
+```
+conda activate unsloth_env
+```
+```
+pip install unsloth
+```
+
+```
+from unsloth import FastModel
+from transformers import CsmForConditionalGeneration
+import torch
+
+gpu_stats = torch.cuda.get_device_properties(0)
+start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
+print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
+print(f"{start_gpu_memory} GB of memory reserved.")
+
+from unsloth import FastLanguageModel as FastModel
+from peft import PeftModel
+from IPython.display import Audio
+
+# --- 1. Load the Base Unsloth Model and Processor ---
+# This setup must be identical to your training script.
+print("Loading the base model and processor...")
+model, processor = FastModel.from_pretrained(
+    model_name = "unsloth/csm-1b",
+    max_seq_length = 2048,
+    dtype = None,
+    auto_model = CsmForConditionalGeneration,
+    load_in_4bit = False,
+)
+
+# --- 2. Identify and Load Your Best LoRA Checkpoint ---
+# !!! IMPORTANT: Change this path to your best checkpoint folder !!!
+# (The one you found in trainer_state.json)
+int_check = 30_000
+
+final_int =94_764
+best_checkpoint_path = "./training_outputs_second_run/checkpoint-"+str(final_int) 
+
+print(f"\nLoading and merging the LoRA adapter from: {best_checkpoint_path}")
+
+# This command seamlessly merges your trained adapter weights onto the base model
+model = PeftModel.from_pretrained(model, best_checkpoint_path)
+
+print("\nFine-tuned model is ready for inference!")
+# Unsloth automatically handles moving the model to the GPU
+```
+
+```
+from transformers import AutoProcessor
+processor = AutoProcessor.from_pretrained("unsloth/csm-1b")
+```
+
+```
+greek_sentences = [
+    "Σου μιλάααανε!",
+    "Γεια σας, είμαι η Μίρα και σήμερα θα κάνουμε μάθημα Ελληνικων.",
+    "Ημουν εξω με φιλους και τα επινα. Μου αρεσει πολυ η μπυρα αλφα!",
+    "Όταν ξανά άνοιξα τα μάτια διαπίστωσα ότι ήμουν ξαπλωμένος σε ένα μαλακό στρώμα από κουβέρτες",
+]
+```
+
+```
+from IPython.display import Audio, display
+import soundfile as sf
+```
+
+```
+# --- Configure the Generation ---
+
+int_ = 1
+text_to_synthesize = greek_sentences[int_]
+
+print(f"\nSynthesizing text: '{text_to_synthesize}'")
+
+speaker_id = 0
+inputs = processor(f"[{speaker_id}]{text_to_synthesize}", add_special_tokens=True).to("cuda")
+
+audio_values = model.generate(
+    **inputs,
+    max_new_tokens=125, # 125 tokens is 10 seconds of audio, for longer speech increase this
+    # play with these parameters to tweak results
+    # depth_decoder_top_k=0,
+    # depth_decoder_top_p=0.9,
+    # depth_decoder_do_sample=True,
+    # depth_decoder_temperature=0.9,
+    # top_k=0,
+    # top_p=1.0,
+    # temperature=0.9,
+    # do_sample=True,
+    #########################################################
+    output_audio=True
+)
+```
+
+```
+audio = audio_values[0].to(torch.float32).cpu().numpy()
+sf.write("example_without_context.wav", audio, 24000)
+display(Audio(audio, rate=24000))
+```