Add instructions on how to run the model with transformers (#31)

- Update README.md (f2b0fc04dc4a811841bab00bcc5986d4a83004b2)

Co-authored-by: Younes Belkada <ybelkada@users.noreply.huggingface.co>

README.md

@@ -1,26 +1,139 @@
 ---
-#以下为文本生成的 "tasks"示例，您可以从此网页中了解更多相关信息：https://modelscope.cn/docs/%E4%BB%BB%E5%8A%A1%E7%9A%84%E4%BB%8B%E7%BB%8D
-#tasks:
-#- text-generation
-license: Apache License 2.0
+language:
+- code
+license: other
+tags:
+- code
+inference: false
+license_name: mnpl
+license_link: https://mistral.ai/licences/MNPL-0.1.md
 ---
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
 
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
+# Model Card for Codestral-22B-v0.1
+
+Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried:
+- As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications
+- As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code)
+
+
+## Installation
+
+It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference).
+
 ```
+pip install mistral_inference
+```
+
+## Download
+
+```py
+from huggingface_hub import snapshot_download
+from pathlib import Path
+
+mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
+
+snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+```
+
+### Chat
+
+After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment.
+
+```
+mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256
+```
+
+Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines:
+
+```
+Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number.
+
+fn fibonacci(n: u32) -> u32 {
+    match n {
+        0 => 0,
+        1 => 1,
+        _ => fibonacci(n - 1) + fibonacci(n - 2),
+    }
+}
+
+fn main() {
+    let n = 10;
+    println!("The {}th Fibonacci number is: {}", n, fibonacci(n));
+}
+
+This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers.
+```
+
+
+### Fill-in-the-middle (FIM)
+
+After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed:
+
+```py
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.tokens.instruct.request import FIMRequest
+
+tokenizer = MistralTokenizer.v3()
+model = Transformer.from_folder("~/codestral-22B-240529")
+
+prefix = """def add("""
+suffix = """    return sum"""
+
+request = FIMRequest(prompt=prefix, suffix=suffix)
+
+tokens = tokenizer.encode_fim(request).tokens
+
+out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.decode(out_tokens[0])
+
+middle = result.split(suffix)[0].strip()
+print(middle)
+```
+
+Should give something along the following lines:
+
+```
+num1, num2):
+
+    # Add two numbers
+    sum = num1 + num2
+
+    # return the sum
+```
+
+## Usage with transformers library
+
+This model is also compatible with `transformers` library, first run `pip install -U transformers` then use the snippet below to quickly get started:
+
 ```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('LLM-Research/Codestral-22B-v0.1')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/LLM-Research/Codestral-22B-v0.1.git
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "mistralai/Codestral-22B-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+text = "Hello my name is"
+inputs = tokenizer(text, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem.
+
+## Limitations
+
+The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
+make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
+
+## License
+
+Codestral-22B-v0.1 is released under the `MNLP-0.1` license.
+
+## The Mistral AI Team
+
+Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall