diff --git a/docs/my-website/docs/providers/predibase.md b/docs/my-website/docs/providers/predibase.md
new file mode 100644
index 000000000..3d5bbaef4
--- /dev/null
+++ b/docs/my-website/docs/providers/predibase.md
@@ -0,0 +1,247 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# 🆕 Predibase
+
+LiteLLM supports all models on Predibase
+
+
+## Usage
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+### API KEYS
+```python
+import os 
+os.environ["PREDIBASE_API_KEY"] = ""
+```
+
+### Example Call
+
+```python
+from litellm import completion
+import os
+## set ENV variables
+os.environ["PREDIBASE_API_KEY"] = "predibase key"
+os.environ["PREDIBASE_TENANT_ID"] = "predibase tenant id"
+
+# predibase llama-3 call
+response = completion(
+    model="predibase/llama-3-8b-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: llama-3
+      litellm_params:
+        model: predibase/llama-3-8b-instruct
+        api_key: os.environ/PREDIBASE_API_KEY
+        tenant_id: os.environ/PREDIBASE_TENANT_ID
+  ```
+
+
+
+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="llama-3",
+      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": "llama-3",
+      "messages": [
+        {
+            "role": "system",
+            "content": "Be a good human!"
+        },
+        {
+            "role": "user",
+            "content": "What do you know about earth?"
+        }
+        ],
+  }'
+  ```
+  </TabItem>
+
+  </Tabs>
+
+
+</TabItem>
+
+</Tabs>
+
+## Advanced Usage - Prompt Formatting 
+
+LiteLLM has prompt template mappings for all `meta-llama` llama3 instruct models. [**See Code**](https://github.com/BerriAI/litellm/blob/4f46b4c3975cd0f72b8c5acb2cb429d23580c18a/litellm/llms/prompt_templates/factory.py#L1360)
+
+To apply a custom prompt template: 
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python 
+import litellm
+
+import os 
+os.environ["PREDIBASE_API_KEY"] = ""
+
+# Create your own custom prompt template 
+litellm.register_prompt_template(
+	    model="togethercomputer/LLaMA-2-7B-32K",
+        initial_prompt_value="You are a good assistant" # [OPTIONAL]
+	    roles={
+            "system": {
+                "pre_message": "[INST] <<SYS>>\n", # [OPTIONAL]
+                "post_message": "\n<</SYS>>\n [/INST]\n" # [OPTIONAL]
+            },
+            "user": { 
+                "pre_message": "[INST] ", # [OPTIONAL]
+                "post_message": " [/INST]" # [OPTIONAL]
+            }, 
+            "assistant": {
+                "pre_message": "\n" # [OPTIONAL]
+                "post_message": "\n" # [OPTIONAL]
+            }
+        }
+        final_prompt_value="Now answer as best you can:" # [OPTIONAL]
+)
+
+def predibase_custom_model():
+    model = "predibase/togethercomputer/LLaMA-2-7B-32K"
+    response = completion(model=model, messages=messages)
+    print(response['choices'][0]['message']['content'])
+    return response
+
+predibase_custom_model()
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+```yaml
+# Model-specific parameters
+model_list:
+  - model_name: mistral-7b # model alias
+    litellm_params: # actual params for litellm.completion()
+      model: "predibase/mistralai/Mistral-7B-Instruct-v0.1" 
+      api_key: os.environ/PREDIBASE_API_KEY
+      initial_prompt_value: "\n"
+      roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}}
+      final_prompt_value: "\n"
+      bos_token: "<s>"
+      eos_token: "</s>"
+      max_tokens: 4096
+```
+
+</TabItem>
+
+</Tabs>
+
+## Passing additional params - max_tokens, temperature 
+See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input)
+
+```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 Predibase specific params - adapter_id, adapter_source, 
+Send params [not supported by `litellm.completion()`](https://docs.litellm.ai/docs/completion/input) but supported by Predibase by passing them to `litellm.completion`
+
+Example `adapter_id`, `adapter_source` are Predibase specific param - [See List](https://github.com/BerriAI/litellm/blob/8a35354dd6dbf4c2fcefcd6e877b980fcbd68c58/litellm/llms/predibase.py#L54)
+
+```python
+# !pip install litellm
+from litellm import completion
+import os
+## set ENV variables
+os.environ["PREDIBASE_API_KEY"] = "predibase key"
+
+# predibase llama3 call
+response = completion(
+    model="predibase/llama-3-8b-instruct", 
+    messages = [{ "content": "Hello, how are you?","role": "user"}],
+    adapter_id="my_repo/3",
+    adapter_soruce="pbase",
+)
+```
+
+**proxy**
+
+```yaml
+  model_list:
+    - model_name: llama-3
+      litellm_params:
+        model: predibase/llama-3-8b-instruct
+        api_key: os.environ/PREDIBASE_API_KEY
+        adapter_id: my_repo/3
+        adapter_source: pbase
+```
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 3c968ea57..4ce587080 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -132,6 +132,8 @@ const sidebars = {
         "providers/cohere", 
         "providers/anyscale",
         "providers/huggingface", 
+        "providers/watsonx",
+        "providers/predibase",
         "providers/ollama", 
         "providers/perplexity", 
         "providers/groq", 
@@ -151,7 +153,7 @@ const sidebars = {
         "providers/openrouter", 
         "providers/custom_openai_proxy",
         "providers/petals",
-        "providers/watsonx",
+        
       ],
     },
     "proxy/custom_pricing",
diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index f726ed95a..630baf346 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -96,7 +96,6 @@ async def test_completion_predibase(sync_mode):
             response = completion(
                 model="predibase/llama-3-8b-instruct",
                 tenant_id="c4768f95",
-                api_base="https://serving.app.predibase.com",
                 api_key=os.getenv("PREDIBASE_API_KEY"),
                 messages=[{"role": "user", "content": "What is the meaning of life?"}],
             )