Doc enhancements to use llama-2

.gitignore

@@ -1,3 +1,4 @@
+tags
 *.cache
 *.pt
 *.pkl

clients/python/llmengine/fine_tuning.py

@@ -283,15 +283,15 @@ def get_events(cls, fine_tune_id: str) -> GetFineTuneEventsResponse:
         Returns:
             GetFineTuneEventsResponse: an object that contains the list of events for the fine-tuning job
 
-        Example:
+        === "Getting events for  fine-tuning jobs in Python"
             ```python
             from llmengine import FineTune
 
             response = FineTune.get_events(fine_tune_id="ft-cir3eevt71r003ks6il0")
             print(response.json())
             ```
 
-        JSON Response:
+        === "Response in JSON"
             ```json
             {
                 "events":

docs/getting_started.md

@@ -48,7 +48,7 @@ With your API key set, you can now send LLM Engine requests using the Python cli
 from llmengine import Completion
 
 response = Completion.create(
-    model="falcon-7b-instruct",
+    model="llama-2-7b",
     prompt="I'm opening a pancake restaurant that specializes in unique pancake shapes, colors, and flavors. List 3 quirky names I could name my restaurant.",
     max_new_tokens=100,
     temperature=0.2,
@@ -66,7 +66,7 @@ import sys
 from llmengine import Completion
 
 stream = Completion.create(
-    model="falcon-7b-instruct",
+    model="llama-2-7b",
     prompt="Give me a 200 word summary on the current economic events in the US.",
     max_new_tokens=1000,
     temperature=0.2,

docs/guides/completions.md

@@ -42,26 +42,21 @@ See the full [Completion API reference documentation](../../api/python_client/#l
 An example Completion API response looks as follows:
 
 === "Response in JSON"
-`python
-    >>> print(response.json())
-    `
-Example output:
-`json
-    {
-      "request_id": "c4bf0732-08e0-48a8-8b44-dfe8d4702fb0",
-      "output": {
-        "text": "_______ and I am a _______",
-        "num_completion_tokens": 10
-      }
-    }
-    `
+    ```python
+        >>> print(response.json())
+        {
+          "request_id": "c4bf0732-08e0-48a8-8b44-dfe8d4702fb0",
+          "output": {
+            "text": "_______ and I am a _______",
+            "num_completion_tokens": 10
+          }
+        }
+    ```
 === "Response in Python"
-`python
-    >>> print(response.output.text)
-    `
-Example output:
-`    _______ and I am a _______
-   `
+    ```python
+        >>> print(response.output.text)
+        _______ and I am a _______
+    ```
 
 ## Token streaming
 
@@ -81,7 +76,7 @@ import sys
 from llmengine import Completion
 
 stream = Completion.create(
-    model="falcon-7b-instruct",
+    model="llama-2-7b",
     prompt="Give me a 200 word summary on the current economic events in the US.",
     max_new_tokens=1000,
     temperature=0.2,

docs/index.md

@@ -30,11 +30,11 @@ Kubernetes.
 ### Key Features
 
 **Ready-to-use APIs for your favorite models**: Deploy and serve
-open source foundation models - including LLaMA, MPT, and Falcon.
+open source foundation models - including Llama-2, MPT, and Falcon.
 Use Scale-hosted models or deploy to your own infrastructure.
 
-**Fine-tune your favorite models**: Fine-tune open-source foundation
-models like LLaMA, MPT, etc. with your own data for optimized performance.
+**Fine-tune the best open-source models**: Fine-tune open-source foundation
+models like Llama-2, MPT, etc. with your own data for optimized performance.
 
 **Optimized Inference**: LLM Engine provides inference APIs
 for streaming responses and dynamically batching inputs for higher throughput