diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md
index 371f9394aff..1c2a599ca8b 100644
--- a/docs/my-website/docs/completion/input.md
+++ b/docs/my-website/docs/completion/input.md
@@ -59,6 +59,7 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea
 |NLP Cloud| ✅ | ✅ | ✅ | ✅ | ✅ | |  |  |  |   |
 |Petals| ✅ | ✅ |  | ✅ | |  |   |  |  |   |
 |Ollama| ✅ | ✅ | ✅ | ✅ | ✅ |  |   | ✅ |  |   | | | ✅ | | |
+|Databricks| ✅ | ✅ | ✅ | ✅ | ✅ |  |   | |  |   | | | | | |
 
 :::note
 
diff --git a/docs/my-website/docs/providers/databricks.md b/docs/my-website/docs/providers/databricks.md
new file mode 100644
index 00000000000..08a3e4f7630
--- /dev/null
+++ b/docs/my-website/docs/providers/databricks.md
@@ -0,0 +1,202 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# 🆕 Databricks
+
+LiteLLM supports all models on Databricks
+
+
+## Usage
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+### ENV VAR
+```python
+import os 
+os.environ["DATABRICKS_API_KEY"] = ""
+os.environ["DATABRICKS_API_BASE"] = ""
+```
+
+### Example Call
+
+```python
+from litellm import completion
+import os
+## set ENV variables
+os.environ["DATABRICKS_API_KEY"] = "databricks key"
+os.environ["DATABRICKS_API_BASE"] = "databricks base url" # e.g.: https://adb-3064715882934586.6.azuredatabricks.net/serving-endpoints
+
+# predibase llama-3 call
+response = completion(
+    model="databricks/databricks-dbrx-instruct", 
+    messages = [{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Add models to your config.yaml
+
+  ```yaml
+  model_list:
+    - model_name: dbrx-instruct
+      litellm_params:
+        model: databricks/databricks-dbrx-instruct
+        api_key: os.environ/DATABRICKS_API_KEY
+        api_base: os.environ/DATABRICKS_API_BASE
+  ```
+
+
+
+2. Start the proxy 
+
+  ```bash
+  $ litellm --config /path/to/config.yaml --debug
+  ```
+
+3. Send Request to LiteLLM Proxy Server
+
+  <Tabs>
+
+  <TabItem value="openai" label="OpenAI Python v1.0.0+">
+
+  ```python
+  import openai
+  client = openai.OpenAI(
+      api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
+      base_url="http://0.0.0.0:4000" # litellm-proxy-base url
+  )
+
+  response = client.chat.completions.create(
+      model="dbrx-instruct",
+      messages = [
+        {
+            "role": "system",
+            "content": "Be a good human!"
+        },
+        {
+            "role": "user",
+            "content": "What do you know about earth?"
+        }
+    ]
+  )
+
+  print(response)
+  ```
+
+  </TabItem>
+
+  <TabItem value="curl" label="curl">
+
+  ```shell
+  curl --location 'http://0.0.0.0:4000/chat/completions' \
+      --header 'Authorization: Bearer sk-1234' \
+      --header 'Content-Type: application/json' \
+      --data '{
+      "model": "dbrx-instruct",
+      "messages": [
+        {
+            "role": "system",
+            "content": "Be a good human!"
+        },
+        {
+            "role": "user",
+            "content": "What do you know about earth?"
+        }
+        ],
+  }'
+  ```
+  </TabItem>
+
+  </Tabs>
+
+
+</TabItem>
+
+</Tabs>
+
+## Passing additional params - max_tokens, temperature 
+See all litellm.completion supported params [here](../completion/input.md#translated-openai-params)
+
+```python
+# !pip install litellm
+from litellm import completion
+import os
+## set ENV variables
+os.environ["PREDIBASE_API_KEY"] = "predibase key"
+
+# predibae llama-3 call
+response = completion(
+    model="predibase/llama3-8b-instruct", 
+    messages = [{ "content": "Hello, how are you?","role": "user"}],
+    max_tokens=20,
+    temperature=0.5
+)
+```
+
+**proxy**
+
+```yaml
+  model_list:
+    - model_name: llama-3
+      litellm_params:
+        model: predibase/llama-3-8b-instruct
+        api_key: os.environ/PREDIBASE_API_KEY
+        max_tokens: 20
+        temperature: 0.5
+```
+
+## Passings Database specific params - 'instruction'
+
+For embedding models, databricks lets you pass in an additional param 'instruction'. [Full Spec](https://github.com/BerriAI/litellm/blob/43353c28b341df0d9992b45c6ce464222ebd7984/litellm/llms/databricks.py#L164)
+
+
+```python
+# !pip install litellm
+from litellm import embedding
+import os
+## set ENV variables
+os.environ["DATABRICKS_API_KEY"] = "databricks key"
+os.environ["DATABRICKS_API_BASE"] = "databricks url"
+
+# predibase llama3 call
+response = litellm.embedding(
+      model="databricks/databricks-bge-large-en",
+      input=["good morning from litellm"],
+      instruction="Represent this sentence for searching relevant passages:",
+  )
+```
+
+**proxy**
+
+```yaml
+  model_list:
+    - model_name: bge-large
+      litellm_params:
+        model: databricks/databricks-bge-large-en
+        api_key: os.environ/DATABRICKS_API_KEY
+        api_base: os.environ/DATABRICKS_API_BASE
+        instruction: "Represent this sentence for searching relevant passages:"
+```
+
+
+## Supported Databricks Chat Completion Models 
+Here's an example of using a Databricks models with LiteLLM
+
+| Model Name                 | Command                                                          |
+|----------------------------|------------------------------------------------------------------|
+| databricks-dbrx-instruct    | `completion(model='databricks/databricks-dbrx-instruct', messages=messages)`   | 
+| databricks-meta-llama-3-70b-instruct    | `completion(model='databricks/databricks-meta-llama-3-70b-instruct', messages=messages)`   | 
+| databricks-llama-2-70b-chat    | `completion(model='databricks/databricks-llama-2-70b-chat', messages=messages)`   | 
+| databricks-mixtral-8x7b-instruct    | `completion(model='databricks/databricks-mixtral-8x7b-instruct', messages=messages)`   | 
+| databricks-mpt-30b-instruct    | `completion(model='databricks/databricks-mpt-30b-instruct', messages=messages)`   | 
+| databricks-mpt-7b-instruct    | `completion(model='databricks/databricks-mpt-7b-instruct', messages=messages)`   | 
+
+## Supported Databricks Embedding Models 
+Here's an example of using a databricks models with LiteLLM
+
+| Model Name                 | Command                                                          |
+|----------------------------|------------------------------------------------------------------|
+| databricks-bge-large-en    | `completion(model='databricks/databricks-bge-large-en', messages=messages)`   | 
diff --git a/docs/my-website/docs/providers/predibase.md b/docs/my-website/docs/providers/predibase.md
index 3d5bbaef417..31713aef1e8 100644
--- a/docs/my-website/docs/providers/predibase.md
+++ b/docs/my-website/docs/providers/predibase.md
@@ -1,7 +1,7 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# 🆕 Predibase
+# Predibase
 
 LiteLLM supports all models on Predibase
 
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index f840ed7897f..fddbf283829 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -133,6 +133,7 @@ const sidebars = {
         "providers/cohere", 
         "providers/anyscale",
         "providers/huggingface", 
+        "providers/databricks",
         "providers/watsonx",
         "providers/predibase",
         "providers/triton-inference-server",