bmedi
diff --git a/‎docs/server.md
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+5-3Lines changed: 5 additions & 3 deletions b/‎docs/server.md
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+5-3Lines changed: 5 additions & 3 deletions
diff --git a/‎examples/notebooks/Functions.ipynb
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+1-1Lines changed: 1 addition & 1 deletion
@@ -76,12 +76,14 @@ Function calling is completely compatible with the OpenAI function calling API a
 
 You'll first need to download one of the available function calling models in GGUF format:
 
-- [functionary-7b-v1](https://huggingface.co/abetlen/functionary-7b-v1-GGUF)
+- [functionary](https://huggingface.co/meetkai)
 
-Then when you run the server you'll need to also specify the `functionary` chat_format
+Then when you run the server you'll need to also specify either `functionary-v1` or `functionary-v2` chat_format.
+
+Note that since functionary requires a HF Tokenizer due to discrepancies between llama.cpp and HuggingFace's tokenizers as mentioned [here](https://github.com/abetlen/llama-cpp-python/blob/main?tab=readme-ov-file#function-calling), you will need to pass in the path to the tokenizer too. The tokenizer files are already included in the respective HF repositories hosting the gguf files.
 
 ```bash
-python3 -m llama_cpp.server --model <model_path> --chat_format functionary
+python3 -m llama_cpp.server --model <model_path_to_functionary_v2_model> --chat_format functionary-v2 --hf_pretrained_model_name_or_path <model_path_to_functionary_v2_tokenizer>
 ```
 
 Check out this [example notebook](https://github.com/abetlen/llama-cpp-python/blob/main/examples/notebooks/Functions.ipynb) for a walkthrough of some interesting use cases for function calling.
 
@@ -9,7 +9,7 @@
     "The OpenAI compatbile web server in `llama-cpp-python` supports function calling.\n",
     "\n",
     "Function calling allows API clients to specify a schema that gives the model a format it should respond in.\n",
-    "Function calling in `llama-cpp-python` works by combining models pretrained for function calling such as [`functionary`](https://huggingface.co/abetlen/functionary-7b-v1-GGUF) with constrained sampling to produce a response that is compatible with the schema.\n",
+    "Function calling in `llama-cpp-python` works by combining models pretrained for function calling such as [`functionary`](https://huggingface.co/meetkai) with constrained sampling to produce a response that is compatible with the schema.\n",
     "\n",
     "Note however that this improves but does not guarantee that the response will be compatible with the schema.\n",
     "\n",