Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- timdettmers/openassistant-guanaco
+pipeline_tag: text-generation
+---
+# Llama-2-13b-guanaco
+
+📝 [Article](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) |
+💻 [Colab](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing) |
+📄 [Script](https://gist.github.com/mlabonne/b5718e1b229ce6553564e3f56df72c5c)
+
+<center><img src="https://i.imgur.com/C2x7n2a.png" width="300"></center>
+
+This is a `llama-2-13b-chat-hf` model fine-tuned using QLoRA (4-bit precision) on the [`mlabonne/guanaco-llama2`](https://huggingface.co/datasets/mlabonne/guanaco-llama2) dataset.
+
+## 🔧 Training
+
+It was trained on a Google Colab notebook with a T4 GPU and high RAM.
+
+## 💻 Usage
+
+``` python
+# pip install transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "mlabonne/llama-2-13b-miniguanaco"
+prompt = "What is a large language model?"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+sequences = pipeline(
+    f'<s>[INST] {prompt} [/INST]',
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+    max_length=200,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```