Create README.md

README.md

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+# Belt_Road_Hungarian 
+
+## Model Description
+
+This model is a conversational and instruction-following large language model, fine-tuned from the foundational open-source **Qwen2.5-72B-Instruct** model using supervised fine-tuning (SFT).
+
+-----
+
+## Key Features & Use Cases
+
+  * **Exceptional Hungarian Language Proficiency:** The model has been deeply optimized for Hungarian, demonstrating excellent fluency, accuracy, and a strong understanding of cultural context in conversations.
+  * **Multilingual Translation and Dialogue:** With extensive training data that includes Hungarian, English, and Chinese content, the model excels in translation, multilingual Q\&A, and cross-language communication.
+  * **Advanced Instruction Following:** The model shows a strong ability to comprehend and execute complex instructions, including those with multiple steps and specific constraints.
+  * **Creative Content Generation:** It is highly suitable for a wide range of creative tasks, such as writing articles, reports, scripts, and marketing copy.
+
+-----
+
+## System Requirements
+
+### Hardware
+
+  * **GPU VRAM:** For BF16/FP16 inference (recommended), at least **4 x NVIDIA A100 (80GB)** GPUs are required. The model weights alone are approximately 136GB, so `device_map="auto"` is necessary to distribute them across multiple cards.
+  * **System RAM:** A minimum of **200GB** is recommended.
+
+### Software
+
+  * **Python:** Version 3.10 or higher.
+  * **Key Libraries:**
+      * `torch`: 2.1 or higher
+      * `transformers`: 4.41.0 or higher
+      * `accelerate`: 1.7.0 or higher
+      * `einops`: 0.8.1 or higher
+      * `sentencepiece`: 0.2.0 or higher
+
+-----
+
+## How to Use
+
+The recommended method for loading and running the model is by using the **`transformers`** library.
+
+```python
+# Example code snippet for inference using transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Specify the model path or your Hugging Face Hub repository
+model_path = "your-huggingface-repo/your-model-name" # e.g., "your-user/your-qwen-model"
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+# Load the model with device_map to distribute it across available GPUs
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16, # or torch.float16
+    device_map="auto"
+)
+
+# Example conversation prompt
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello, can you translate 'hello' to Hungarian and Chinese?"}
+]
+
+# Apply the chat template and generate a response
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```