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README.md

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+---
+base_model: google/gemma-2-27b-it
+datasets:
+- DiTy/function-calling
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- conversational
+- gemma2
+- function-calling
+- trl
+---
+# DiTy/gemma-2-27b-it-function-calling-GGUF
+
+This model is a fine-tuned version of [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) for the **Function Calling** task on non-synthetic data, 
+fully annotated by humans only, on the English version of the <ins>*DiTy/function-calling*</ins> dataset.
+<!-- Provide a quick summary of what the model is/does. -->
+
+In addition to **safetensors**, the model is available in **GGUF** formats (in this case, you need to download only a single file (*[how to inference GGUF model](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#high-level-api)*)):
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [gemma-2-27B-it-function-calling-Q8_0.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q8_0.gguf) | Q8_0 | 28.9GB | Extremely high quality, generally unneeded but max available quant. |
+| [gemma-2-27B-it-function-calling-Q6_K.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q6_K.gguf) | Q6_K | 22.3GB | Very high quality, near perfect, *recommended*. |
+| [gemma-2-27B-it-function-calling-Q5_K_M.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q5_K_M.gguf) | Q5_K_M | 19.4GB | High quality, very usable. |
+| [gemma-2-27B-it-function-calling-Q5_K_S.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q5_K_S.gguf) | Q5_K_S | 18.9GB | High quality, very usable. |
+
+
+## Model card tree
+
+* [How prepare your functions (tools) for *Function Calling*](#prepare_func_call)
+* [Just use chat template for generation](#just_chat_template)
+* [Prompt structure and expected content](#roles)
+* [Evaluation of function calling models](#eval)
+
+## Usage (HuggingFace Transformers)
+
+Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
+```bash
+pip install -U transformers
+```
+
+### <a name="prepare_func_call"></a>Prepare your functions for *Function Calling*
+
+You should write the functions (tools) used by the model in *Python code* and make sure to add *Python docstrings* as in the example below:
+```python
+def get_weather(city: str):
+    """
+    A function that returns the weather in a given city.
+    
+    Args:
+        city: The city to get the weather for.
+    """
+    import random
+    
+    return "sunny" if random.random() > 0.5 else "rainy"
+def get_sunrise_sunset_times(city: str):
+    """
+    A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].
+    
+    Args:
+        city: The city to get the sunrise and sunset times for.
+    """
+    
+    return ["6:00 AM", "6:00 PM"]
+```
+
+### <a name="just_chat_template"></a>Just use chat template
+
+Next, you need to download the model and tokenizer:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(
+    "DiTy/gemma-2-27b-it-function-calling-GGUF",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,  # use float16 or float32 if bfloat16 is not available to you.
+    cache_dir=PATH_TO_MODEL_DIR,  # optional
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "DiTy/gemma-2-27b-it-function-calling-GGUF",
+    cache_dir=PATH_TO_MODEL_DIR,  # optional
+)
+```
+
+To get the result of generation, just use `apply_chat_template`. In order to take into account our written functions (tools), 
+we need to pass them as a list through the `tools` attribute and also use `add_prompt_generation=True`.
+```python
+history_messages = [
+    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
+    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
+]
+inputs = tokenizer.apply_chat_template(
+    history_messages,
+    tokenize=False,
+    add_generation_prompt=True,  # adding prompt for generation
+    tools=[get_weather, get_sunrise_sunset_times],  # our functions (tools)
+)
+print(inputs)
+```
+
+Then our `inputs` will look like this:
+```
+<bos><start_of_turn>user
+You are a helpful assistant with access to the following functions. Use them if required - {
+    "name": "get_weather",
+    "description": "A function that returns the weather in a given city.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city to get the weather for."
+            }
+        },
+        "required": [
+            "city"
+        ]
+    }
+},
+{
+    "name": "get_sunrise_sunset_times",
+    "description": "A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city to get the sunrise and sunset times for."
+            }
+        },
+        "required": [
+            "city"
+        ]
+    }
+}
+Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
+<start_of_turn>model
+```
+
+Now we can generate a model's response. 
+Be careful because, after `apply_chat_template`, there is no need to *add special tokens* during tokenization. So, use `add_special_tokens=False`:
+```python
+terminator_ids = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<end_of_turn>"),
+]
+prompt_ids =  tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
+generated_ids = model.generate(
+    prompt_ids,
+    max_new_tokens=512,
+    eos_token_id=terminator_ids,
+    bos_token_id=tokenizer.bos_token_id,
+)
+generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False)  # `skip_special_tokens=False` for debug
+print(generated_response)
+```
+
+We get the generation as a function call:
+```
+Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
+```
+
+Great, now we can pick up and process the results with our *called function*, and then provide the model with the *function's response*:
+```python
+history_messages = [
+    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
+    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
+    {"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
+    {"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},  # a hypothetical response from our function
+]
+inputs = tokenizer.apply_chat_template(
+    history_messages,
+    tokenize=False,
+    add_generation_prompt=True,  # adding prompt for generation
+    tools=[get_weather, get_sunrise_sunset_times],  # our functions (tools)
+)
+print(inputs)
+```
+
+Let's make sure the `inputs` are correct:
+```
+<bos><start_of_turn>user
+You are a helpful assistant with access to the following functions. Use them if required - {
+    "name": "get_weather",
+    "description": "A function that returns the weather in a given city.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city to get the weather for."
+            }
+        },
+        "required": [
+            "city"
+        ]
+    }
+},
+{
+    "name": "get_sunrise_sunset_times",
+    "description": "A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city to get the sunrise and sunset times for."
+            }
+        },
+        "required": [
+            "city"
+        ]
+    }
+}
+Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
+<start_of_turn>model
+Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
+<start_of_turn>user
+Function response: {"times_list": ["6:00 AM", "6:00 PM"]}<end_of_turn>
+<start_of_turn>model
+```
+
+Similarly, we generate a response from the model:
+```python
+prompt_ids =  tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
+generated_ids = model.generate(
+    prompt_ids,
+    max_new_tokens=512,
+    eos_token_id=terminator_ids,
+    bos_token_id=tokenizer.bos_token_id,
+)
+generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False)  # `skip_special_tokens=False` for debug
+print(generated_response)
+```
+
+As a result, we get the model's response:
+```
+The sunrise time in Los Angeles is 6:00 AM.<end_of_turn>
+```
+
+## Usage via transformers `pipeline`
+
+<details>
+  <summary>
+  Generation via pipeline
+  </summary>
+
+```python
+from transformers import pipeline
+generation_pipeline = pipeline(
+    "text-generation",
+    model="DiTy/gemma-2-27b-it-function-calling-GGUF",
+    model_kwargs={
+        "torch_dtype": torch.bfloat16,  # use float16 or float32 if bfloat16 is not supported for you. 
+        "cache_dir": PATH_TO_MODEL_DIR,  # OPTIONAL
+    },
+    device_map="auto",
+)
+history_messages = [
+    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
+    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
+    {"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
+    {"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},
+]
+inputs = generation_pipeline.tokenizer.apply_chat_template(
+    history_messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    tools=[get_weather, get_sunrise_sunset_times],
+)
+terminator_ids = [
+    generation_pipeline.tokenizer.eos_token_id,
+    generation_pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
+]
+outputs = generation_pipeline(
+    inputs,
+    max_new_tokens=512,
+    eos_token_id=terminator_ids,
+)
+print(outputs[0]["generated_text"][len(inputs):])
+```
+  
+</details>
+
+## <a name="roles"></a>Prompt structure and expected content
+
+For the most correct operation of the model, it is assumed that `apply_chat_template` will be used. 
+It is necessary to transmit the message history in a certain format.
+```python
+history_messages = [
+    {"role": "...", "content": "..."},
+    ...
+]
+```
+
+The following roles are available for use:
+
+* `system` - an optional role, its content is always placed at the very beginning and before listing the functions available to the model (tools).
+You can always use the standard option that was used during the training: ***"You are a helpful assistant with access to the following functions. Use them if required - "***
+* `user` - the user's request is transmitted through this role.
+* `function-call` - The body of the function call is passed through this role.
+Although the model is trained to generate a function call in the form of ***"Function call: {...}\<end_of_turn\>"***, you should still pass only the body ***"{...}"***
+to the *"content"* field, since using `apply_chat_template`, the postscript in the instructions is added automatically.
+* `function-response` - in this role, we must pass the response of our function in the *"content"* field as a dictionary ***'{"name_returnable_value": value}'***.
+* `model` - the content under this role is considered to be the generated text of the model.
+
+### Chat history with *Function Calling*
+
+```
+[
+    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
+    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
+    {"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
+    {"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},
+]
+```
+
+It looks like:
+```
+<bos><start_of_turn>user
+You are a helpful assistant with access to the following functions. Use them if required - {
+    "name": "get_weather",
+    "description": "A function that returns the weather in a given city.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city to get the weather for."
+            }
+        },
+        "required": [
+            "city"
+        ]
+    }
+},
+{
+    "name": "get_sunrise_sunset_times",
+    "description": "A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city to get the sunrise and sunset times for."
+            }
+        },
+        "required": [
+            "city"
+        ]
+    }
+}
+Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
+<start_of_turn>model
+Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
+<start_of_turn>user
+Function response: {"times_list": ["6:00 AM", "6:00 PM"]}<end_of_turn>
+```
+
+
+### Chat history with a standard user-model template
+
+```
+[
+    {"role": "system", "content": "You are a helpful assistant"},
+    {"role": "user", "content": "Tell me about California"},
+]
+```
+
+It looks like:
+```
+<bos><start_of_turn>user
+You are a helpful assistant
+Tell me about California<end_of_turn>
+```
+
+## <a name="eval"></a>Evaluation
+
+During the learning process, the validation error was approximated to the following values:
+
+| **Model** | **Generation Language** | **Approximately Validation Loss** |
+| :-----: | :-----: | :-----: |
+| [**DiTy/gemma-2-27b-it-function-calling-GGUF**](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF) | **EN** | **0.47** |
+| [DiTy/gemma-2-9b-it-russian-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) | RU | 0.57 |
+| [DiTy/gemma-2-9b-it-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF) | EN | 0.5 |
+| [DiTy/gemma-2-2b-it-function-calling](https://huggingface.co/DiTy/gemma-2-2b-it-function-calling) | EN | 0.66 |
+
+## Citation
+
+```none
+@article{gemma_2024,
+    title={Gemma},
+    url={https://www.kaggle.com/m/3301},
+    DOI={10.34740/KAGGLE/M/3301},
+    publisher={Kaggle},
+    author={Gemma Team},
+    year={2024}
+}
+```

gemma-2-27b-it-function-calling-gguf.Q4_0.gguf

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