Create README.md

README.md

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+---
+# YAML front matter for repository configuration
+language: en # Changed language to English
+license: llama3 # Verify the correct Llama 3 license identifier
+tags:
+- llama-3.1
+- qlora
+- sentiment-analysis
+- turkish
+- text-classification
+- peft
+pipeline_tag: text-classification
+widget:
+- text: "This movie was amazing, I recommend it to everyone!" # English example
+- text: "The product quality was much lower than I expected." # English example
+- text: "The meeting will be held tomorrow at 10 AM." # English example
+---
+
+# Llama-3.1-8B-Instruct Fine-tuned for Turkish Sentiment Analysis (QLoRA)
+
+This repository contains a version of the `meta-llama/Llama-3.1-8B-Instruct` model fine-tuned for the Turkish sentiment analysis task using the [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset) dataset and the QLoRA (4-bit) method.
+
+**Model Name:** `ceofast/llama3.1-8b-instruct-turkish-sentiment-qlora`
+
+## Model Description
+
+This model is trained to classify the sentiment of a given Turkish text as **positive**, **negative**, or **neutral**. The QLoRA (Quantized Low-Rank Adaptation) technique enables fine-tuning large language models using significantly fewer computational resources. This specific model was trained using 4-bit quantization.
+
+*   **Base Model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+*   **Fine-tuning Technique:** QLoRA (4-bit NF4)
+*   **Language:** Turkish (tr)
+*   **Task:** Text Classification (Sentiment Analysis)
+*   **Labels:** `LABEL_0` (negative), `LABEL_1` (neutral), `LABEL_2` (positive)
+
+## How to Use
+
+To use this model, you need to have the `transformers`, `peft`, `accelerate`, `bitsandbytes`, and `torch` libraries installed.
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, BitsAndBytesConfig
+
+# Base model ID
+base_model_id = "meta-llama/Llama-3.1-8B-Instruct"
+# QLoRA adapter ID (this repository)
+adapter_id = "ceofast/llama3.1-8b-instruct-turkish-sentiment-qlora"
+# Labels
+labels = ["negative", "neutral", "positive"]
+
+# 4-bit quantization configuration
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16 # Or float16 depending on your GPU
+)
+
+# Load the base model in 4-bit
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    base_model_id,
+    num_labels=len(labels),
+    quantization_config=bnb_config,
+    device_map="auto", # Load model to appropriate device (GPU/CPU)
+    trust_remote_code=True, # If required by the base model
+    # Add your HF Token here or ensure you are logged in
+    # token="YOUR_HF_TOKEN"
+    # Suppress classification head mismatch warning
+    ignore_mismatched_sizes=True
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+# Set PAD token for Llama
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+base_model.config.pad_token_id = tokenizer.pad_token_id
+
+# Load the PEFT adapter and merge it with the base model
+# Note: For inference, merging is often not necessary, you can directly use the PeftModel
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval() # Set the model to evaluation mode
+
+# Inference function
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    # Move inputs to the same device as the model
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predictions = torch.argmax(logits, dim=-1)
+        return labels[predictions.item()]
+
+# Example usage
+text1 = "Bu film tek kelimeyle muhteşemdi!" # Keeping original Turkish examples
+text2 = "Kargo çok geç geldi ve ürün hasarlıydı."
+text3 = "Hava bugün güneşli."
+text4 = "This restaurant is fantastic!" # Added an English example
+
+print(f"'{text1}' -> Sentiment: {predict_sentiment(text1)}")
+print(f"'{text2}' -> Sentiment: {predict_sentiment(text2)}")
+print(f"'{text3}' -> Sentiment: {predict_sentiment(text3)}")
+print(f"'{text4}' -> Sentiment: {predict_sentiment(text4)}") # Note: Model is trained on Turkish
+```
+# Expected Output (example):
+# 'Bu film tek kelimeyle muhteşemdi!' -> Sentiment: positive
+# 'Kargo çok geç geldi ve ürün hasarlıydı.' -> Sentiment: negative
+# 'Hava bugün güneşli.' -> Sentiment: neutral
+# 'This restaurant is fantastic!' -> Sentiment: positive (Might work for simple English, but primarily Turkish)
+
+
+
+**Key Changes Made:**
+
+*   Changed `language: tr` to `language: en` in the YAML front matter.
+*   Translated all headings and descriptive text.
+*   Translated comments within the Python code block.
+*   Added an English example to the `widget` and the "How to Use" section, while keeping the Turkish ones.
+*   Kept technical terms and IDs the same.
+*   **Hardware:** 1x NVIDIA RTX 3060 (Laptop, Max Performance, 6GB VRAM)
+
+Remember to fill in the bracketed placeholders (`[...]`) with your specific evaluation results and hardware details!