Update README.md

README.md

@@ -1,3 +1,49 @@
 ---
 license: apache-2.0
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+tags:
+- TinyLlama
+- QLoRA
+- Politics
+- News
+- sft
+language:
+- en
+pipeline_tag: text-generation
 ---
+
+# TinyNewsLlama-1.1B
+
+TinyNewsLlama-1.1B is a QLoRA SFT fine-tune of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using a sample of a concentrated version of the [bigNews] (https://paperswithcode.com/dataset/bignews) Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.
+
+The goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.
+
+The used training data contains political news articles from **The New York Times**, **USA Today** and **The Washington Times**. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.
+
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+from transformers import AutoTokenizer
+import transformers
+import torch
+model = "h4rz3rk4s3/TinyNewsLlama-1.1B"
+messages = [
+    {
+        "role": "system",
+        "content": "You are a an experienced journalist.",
+    },
+    {"role": "user", "content": "Write a short article on Brexit and it's impact on the European Union."},
+]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    device_map="auto",
+)
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```