Update README.md

add a short example of transformers lib usage

README.md

@@ -42,6 +42,33 @@ You can then start chatting with the model, *e.g.* prompt it with something like
 
 *"Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?"*
 
+### Usage in `transformers`
+
+To use this model within the `transformers` library, install the latest release with `pip install --upgrade transformers` and run, for instance:
+
+```py
+from transformers import MistralForCausalLM
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('mistralai/mathstral-7B-v0.1')
+
+prompt = "What are the roots of unity?"
+tokenized_prompts = tokenizer(prompt, return_tensors="pt") 
+
+model = MistralForCausalLM.from_pretrained('mistralai/mathstral-7B-v0.1')
+generation = model.generate(**tokenized_prompts, max_new_tokens=512)
+print(tokenizer.decode(generation[0]))
+>>> """<s>What are the roots of unity?
+
+The roots of unity are the solutions to the equation $z^n = 1$, where $n$ is a positive integer.
+These roots are complex numbers and they form a regular $n$-gon in the complex plane.
+
+For example, the roots of unity for $n=1$ are just $1$,
+and for $n=2$ they are $1$ and $-1$. For $n=3$, they are $1$, $\\frac{-1+\\sqrt{3}i}{2}$, and $\\frac{-1-\\sqrt{3}i}{2}$.
+
+The roots of unity have many interesting properties and they are used in many areas of mathematics, including number theory, algebra, and geometry.</s>"""
+```
+
 ## Evaluation
 We evaluate Mathstral 7B and open-weight models of the similar size on industry-standard benchmarks.
 | Benchmarks | MATH | GSM8K (8-shot) | Odyssey Math maj@16 | GRE Math maj@16 | AMC 2023 maj@16 | AIME 2024 maj@16