Update version to v0.0.111

.gitignore

@@ -1,3 +1,6 @@
+# VSCode settings
+.vscode/
+
 # Logs
 logs
 *.log

docs/capabilities/function-calling.mdx

@@ -16,11 +16,11 @@ Function calling allows Mistral models to connect to external tools. By integrat
 Currently, function calling is available for the following models:
 - Mistral Large 
 - Mistral Small 
-- Codestral 22B 
+- Codestral 
 - Ministral 8B 
 - Ministral 3B 
 - Pixtral 12B 
-- Mixtral 8x22B 
+- Pixtral Large
 - Mistral Nemo 
 
 

docs/capabilities/predicted-outputs.md

@@ -0,0 +1,150 @@
+---
+id: predicted-outputs
+title: Predicted outputs
+sidebar_position: 2.92
+---
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+
+Predicted Outputs optimizes response time by leveraging known or predictable content. 
+This approach minimizes latency while maintaining high output quality. In tasks such as editing large texts, modifying code, or generating template-based responses, significant portions of the output are often predetermined. By predefining these expected parts with Predicted Outputs, models can allocate more computational resources to the unpredictable elements, improving overall efficiency.
+
+## Example: Code modification
+
+Predicted Outputs shine in scenarios where you need to regenerate text documents or code files with minor modifications. The key parameter introduced is the `prediction` parameter, which enables users to define predicted outputs. For example, imagine you want your model to update the model used in a fine-tuning job. You can include the code snippet you'd like to modify as both the user prompt and the predicted output.
+
+<Tabs>
+  <TabItem value="python" label="python" default>
+```python
+import os
+from mistralai import Mistral
+
+api_key = os.environ["MISTRAL_API_KEY"]
+model = "mistral-large-latest"
+
+client = Mistral(api_key=api_key)
+
+code = """
+created_jobs = client.fine_tuning.jobs.create(
+    model="open-mistral-7b", 
+    training_files=[{"file_id": ultrachat_chunk_train.id, "weight": 1}],
+    validation_files=[ultrachat_chunk_eval.id], 
+    hyperparameters={
+        "training_steps": 10,
+        "learning_rate":0.0001
+    },
+    auto_start=False
+)
+"""
+
+prompt = "Change the model name from open-mistral-7b to open-mistral-nemo. Respond only with code, no explanation, no formatting."
+
+chat_response = client.chat.complete(
+    model= model,
+    messages = [
+        {
+            "role": "user",
+            "content": prompt,
+        },
+        {
+            "role": "user",
+            "content": code
+        },
+    ],
+    prediction = {
+        "type": "content",
+        "content": code
+    }
+)
+print(chat_response.choices[0].message.content)
+```
+
+  </TabItem>
+
+  <TabItem value="typescript" label="typescript">
+```typescript
+import { Mistral } from '@mistralai/mistralai';
+
+const apiKey = process.env.MISTRAL_API_KEY;
+
+const client = new Mistral({apiKey: apiKey});
+
+const code = `
+created_jobs = client.fine_tuning.jobs.create(
+    model="open-mistral-7b", 
+    training_files=[{"file_id": ultrachat_chunk_train.id, "weight": 1}],
+    validation_files=[ultrachat_chunk_eval.id], 
+    hyperparameters={
+        "training_steps": 10,
+        "learning_rate":0.0001
+    },
+    auto_start=False
+)
+`.trim();
+
+const prompt = `Change the model name from open-mistral-7b to open-mistral-nemo. Respond only with code, no explanation, no formatting.`;
+
+const chatResponse = await client.chat.complete({
+    model: "mistral-large-latest",
+    messages: [
+        {
+            role: 'user', 
+            content: prompt
+        },
+        {
+            role: "user",
+            content: code
+        },
+    ],
+    prediction: {
+        type: "content",
+        content: code 
+    },
+});
+
+console.log('Chat:', chatResponse.choices[0].message.content);
+```
+  </TabItem>
+
+  <TabItem value="curl" label="curl">
+```bash
+curl --location "https://api.mistral.ai/v1/chat/completions" \
+     --header 'Content-Type: application/json' \
+     --header 'Accept: application/json' \
+     --header "Authorization: Bearer $MISTRAL_API_KEY" \
+     --data '{
+    "model": "mistral-large-latest",
+    "messages": [
+        {"role": "user", "content": "Change the model name from open-mistral-7b to open-mistral-nemo. Respond only with code, no explanation, no formatting."},
+        {"role": "user", "content": "$CODE"}
+    ],
+    "prediction": {
+        "type": "content",
+        "content": "$CODE"
+    }
+  }'
+```
+  </TabItem>
+</Tabs>
+
+
+## FAQ
+
+### Which model supports predicted outputs?
+As of now, `codestral-2501` and `mistral-large-2411` support predicted outputs.
+
+### How does predicted outputs affect pricing? 
+Currently, predicted outputs do not impact pricing.
+
+### Which parameters are not supported when using Predicted Outputs?
+`n` (number of completions to return for each request) is not supported when using predicted outputs.
+
+### Does the Position of Certain Sentences or Words in the Prediction Matter?
+No, the placement of sentences or words in your prediction does not affect its effectiveness. Predictions can appear anywhere within the generated response and still help reduce the API's output latency.
+
+
+
+
+
+