Update README.md

README.md

@@ -12,13 +12,6 @@ language:
     <img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
 </center>
 
-##  Table of Contents
-
-1. [Model Summary](##model-summary)
-2. [Limitations](##limitations)
-3. [Training](##training)
-4. [License](##license)
-5. [Citation](##citation)
 
 ## Model Summary
 
@@ -29,6 +22,30 @@ To build SmolLM-Instruct, we instruction tuned the models using publicly availab
 
 This is the SmolLM-360M-Instruct.
 
+### Generation
+```bash
+pip install transformers
+```
+
+```python
+# pip install transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "HuggingFaceTB/SmolLM-1.7B-Instruct"
+
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+messages = [{"role": "user", "content": "List the steps to bake a chocolate cake from scratch."}]
+input_text=tokenizer.apply_chat_template(messages, tokenize=False)
+print(input_text)
+inputs = tokenizer.encode(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(inputs, max_new_tokens=100, temperature=0.6, top_p=0.92, do_sample=True)
+print(tokenizer.decode(outputs[0]))
+```
+
+
 # Limitations
 
 While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post.