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Model: DiTy/gemma-2-9b-it-function-calling-GGUF Source: Original Platform
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---
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base_model: google/gemma-2-9b-it
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datasets:
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- DiTy/function-calling
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language:
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- conversational
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- gemma2
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- function-calling
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- trl
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---
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# DiTy/gemma-2-9b-it-function-calling-GGUF
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This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) for the **Function Calling** task on non-synthetic data,
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fully annotated by humans only, on the English version of the <ins>*DiTy/function-calling*</ins> dataset.
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<!-- Provide a quick summary of what the model is/does. -->
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> [!NOTE]
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> NB: This model has a fairly high quality, but you might want to try a big guy [DiTy/gemma-2-27b-it-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF).
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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)*)):
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [gemma-2-9B-it-function-calling-F16.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF/blob/main/gemma-2-9B-it-function-calling-F16.gguf) | F16 | 18.5GB | Base model with float16 |
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## Model card tree
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* [How prepare your functions (tools) for *Function Calling*](#prepare_func_call)
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* [Just use chat template for generation](#just_chat_template)
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* [Prompt structure and expected content](#roles)
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* [Evaluation of function calling models](#eval)
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## Usage (HuggingFace Transformers)
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Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
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```bash
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pip install -U transformers
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```
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### <a name="prepare_func_call"></a>Prepare your functions for *Function Calling*
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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:
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```python
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def get_weather(city: str):
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"""
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A function that returns the weather in a given city.
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Args:
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city: The city to get the weather for.
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"""
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import random
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return "sunny" if random.random() > 0.5 else "rainy"
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def get_sunrise_sunset_times(city: str):
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"""
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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].
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Args:
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city: The city to get the sunrise and sunset times for.
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"""
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return ["6:00 AM", "6:00 PM"]
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```
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### <a name="just_chat_template"></a>Just use chat template
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Next, you need to download the model and tokenizer:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"DiTy/gemma-2-9b-it-function-calling-GGUF",
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device_map="auto",
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torch_dtype=torch.bfloat16, # use float16 or float32 if bfloat16 is not available to you.
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cache_dir=PATH_TO_MODEL_DIR, # optional
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"DiTy/gemma-2-9b-it-function-calling-GGUF",
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cache_dir=PATH_TO_MODEL_DIR, # optional
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)
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```
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To get the result of generation, just use `apply_chat_template`. In order to take into account our written functions (tools),
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we need to pass them as a list through the `tools` attribute and also use `add_prompt_generation=True`.
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```python
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history_messages = [
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{"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
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{"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
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]
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inputs = tokenizer.apply_chat_template(
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history_messages,
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tokenize=False,
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add_generation_prompt=True, # adding prompt for generation
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tools=[get_weather, get_sunrise_sunset_times], # our functions (tools)
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)
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print(inputs)
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```
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Then our `inputs` will look like this:
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```
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<bos><start_of_turn>user
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You are a helpful assistant with access to the following functions. Use them if required - {
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"name": "get_weather",
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"description": "A function that returns the weather in a given city.",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "The city to get the weather for."
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}
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},
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"required": [
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"city"
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]
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}
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},
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{
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"name": "get_sunrise_sunset_times",
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"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].",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "The city to get the sunrise and sunset times for."
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}
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},
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"required": [
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"city"
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]
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}
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}
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Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
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<start_of_turn>model
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```
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Now we can generate a model's response.
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Be careful because, after `apply_chat_template`, there is no need to *add special tokens* during tokenization. So, use `add_special_tokens=False`:
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```python
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terminator_ids = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<end_of_turn>"),
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]
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prompt_ids = tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
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generated_ids = model.generate(
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prompt_ids,
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max_new_tokens=512,
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eos_token_id=terminator_ids,
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bos_token_id=tokenizer.bos_token_id,
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)
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generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False) # `skip_special_tokens=False` for debug
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print(generated_response)
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```
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We get the generation as a function call:
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```
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Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
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```
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Great, now we can pick up and process the results with our *called function*, and then provide the model with the *function's response*:
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```python
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history_messages = [
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{"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
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{"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
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{"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
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{"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'}, # a hypothetical response from our function
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]
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inputs = tokenizer.apply_chat_template(
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history_messages,
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tokenize=False,
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add_generation_prompt=True, # adding prompt for generation
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tools=[get_weather, get_sunrise_sunset_times], # our functions (tools)
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)
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print(inputs)
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```
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Let's make sure the `inputs` are correct:
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```
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<bos><start_of_turn>user
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You are a helpful assistant with access to the following functions. Use them if required - {
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"name": "get_weather",
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"description": "A function that returns the weather in a given city.",
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"parameters": {
|
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"type": "object",
|
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"properties": {
|
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"city": {
|
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"type": "string",
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"description": "The city to get the weather for."
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}
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},
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"required": [
|
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"city"
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]
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}
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},
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{
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"name": "get_sunrise_sunset_times",
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"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].",
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"parameters": {
|
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"type": "object",
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"properties": {
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"city": {
|
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"type": "string",
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"description": "The city to get the sunrise and sunset times for."
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}
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},
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"required": [
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"city"
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]
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}
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}
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Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
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<start_of_turn>model
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Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
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<start_of_turn>user
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Function response: {"times_list": ["6:00 AM", "6:00 PM"]}<end_of_turn>
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<start_of_turn>model
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```
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Similarly, we generate a response from the model:
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```python
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prompt_ids = tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
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generated_ids = model.generate(
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prompt_ids,
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max_new_tokens=512,
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eos_token_id=terminator_ids,
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bos_token_id=tokenizer.bos_token_id,
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)
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generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False) # `skip_special_tokens=False` for debug
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print(generated_response)
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```
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As a result, we get the model's response:
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```
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The sunrise time in Los Angeles is 6:00 AM.<end_of_turn>
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```
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## Usage via transformers `pipeline`
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<details>
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<summary>
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Generation via pipeline
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</summary>
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```python
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from transformers import pipeline
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generation_pipeline = pipeline(
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"text-generation",
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model="DiTy/gemma-2-9b-it-function-calling-GGUF",
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model_kwargs={
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"torch_dtype": torch.bfloat16, # use float16 or float32 if bfloat16 is not supported for you.
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"cache_dir": PATH_TO_MODEL_DIR, # OPTIONAL
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},
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device_map="auto",
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)
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history_messages = [
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{"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
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{"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
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{"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
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{"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},
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]
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inputs = generation_pipeline.tokenizer.apply_chat_template(
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history_messages,
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tokenize=False,
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add_generation_prompt=True,
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tools=[get_weather, get_sunrise_sunset_times],
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)
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terminator_ids = [
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generation_pipeline.tokenizer.eos_token_id,
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generation_pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
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]
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outputs = generation_pipeline(
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inputs,
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max_new_tokens=512,
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eos_token_id=terminator_ids,
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)
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print(outputs[0]["generated_text"][len(inputs):])
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```
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</details>
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## <a name="roles"></a>Prompt structure and expected content
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For the most correct operation of the model, it is assumed that `apply_chat_template` will be used.
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It is necessary to transmit the message history in a certain format.
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```python
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history_messages = [
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{"role": "...", "content": "..."},
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...
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]
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```
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The following roles are available for use:
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* `system` - an optional role, its content is always placed at the very beginning and before listing the functions available to the model (tools).
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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 - "***
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* `user` - the user's request is transmitted through this role.
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* `function-call` - The body of the function call is passed through this role.
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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 ***"{...}"***
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to the *"content"* field, since using `apply_chat_template`, the postscript in the instructions is added automatically.
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* `function-response` - in this role, we must pass the response of our function in the *"content"* field as a dictionary ***'{"name_returnable_value": value}'***.
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* `model` - the content under this role is considered to be the generated text of the model.
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### Chat history with *Function Calling*
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```
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[
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{"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
|
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{"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
|
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{"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
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{"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},
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]
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```
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It looks like:
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```
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<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
|
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Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
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<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}
|
||||
}
|
||||
```
|
||||
34
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34
special_tokens_map.json
Normal file
34
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Normal file
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||||
{
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3
tokenizer.json
Normal file
3
tokenizer.json
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3
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3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
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2013
tokenizer_config.json
Normal file
2013
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user