Update README.md

README.md

@@ -91,21 +91,37 @@ special_tokens:
 
 # SmolLM-135M-instruct-de
 
-This model is a fine-tuned version of [LemiSt/SmolLM-135M-de](https://huggingface.co/LemiSt/SmolLM-135M-de) on the None dataset.
+This model is a fine-tuned version of [LemiSt/SmolLM-135M-de](https://huggingface.co/LemiSt/SmolLM-135M-de) on an internal testing dataset with general chat examples.
 It achieves the following results on the evaluation set:
 - Loss: 0.7453
 
 ## Model description
 
-More information needed
+For more information, see the mode card of the [base model](https://huggingface.co/LemiSt/SmolLM-135M-de). This adapter was trained using qlora at rank 32 with alpha 16, applying a dataset of around 200k german chat samples for two epochs.
 
 ## Intended uses & limitations
 
-More information needed
-
+Mainly playing around with tiny chat models - while the output is generally intact German and the model somewhat follows instructions, it makes too many mistakes to be deployed in a real world setting.
+
+### Usage example
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+checkpoint = "LemiSt/SmolLM-135M-instruct-de"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map=device, torch_dtype=torch.bfloat16)
+messages = [
+  {"role": "system", "content": "Du bist ein hilfreicher Assistent."},
+  {"role": "user", "content": "Wie viele Hände hat ein normaler Mensch?"}
+]
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt", add_generation_prompt=True).to(device)
+outputs = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.5, top_p=0.9)
+print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
+```
 ## Training and evaluation data
 
-More information needed
+Internal dataset which was compiled for another experiment.
 
 ## Training procedure