Update README.md

README.md

@@ -1,199 +1,408 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- conversational
+- gemma2
+- function-calling
+- trl
+license: apache-2.0
+datasets:
+- DiTy/function-calling
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# DiTy/gemma-2-2b-it-function-calling
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
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-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
+> NB: If you want to use the model to call functions in complex, long and confusing dialogues, it is better to use a larger model [DiTy/gemma-2-9b-it-function-calling](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling).  
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-
-### Results
-
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
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-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-
-## Model Card Contact
+This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-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. -->
 
-[More Information Needed]
+## 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-2-2b-it-function-calling",
+    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-2-2b-it-function-calling",
+    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)
+)
+```
+
+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 
+```
+
+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)
+)
+```
+
+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
+```
+
+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-2-2b-it-function-calling",
+    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-2-9b-it-function-calling](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling) | 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}
+}
+```