Original model description:

license: mit library_name: transformers pipeline_tag: text-generation model-index: - name: Rubra-Phi-3-mini-128k-instruct results: - task: type: text-generation dataset: type: MMLU name: MMLU metrics: - type: 5-shot value: 67.87 verified: false - task: type: text-generation dataset: type: GPQA name: GPQA metrics: - type: 0-shot value: 29.69 verified: false - task: type: text-generation dataset: type: GSM-8K name: GSM-8K metrics: - type: 8-shot, CoT value: 79.45 verified: false - task: type: text-generation dataset: type: MATH name: MATH metrics: - type: 4-shot, CoT value: 30.80 verified: false - task: type: text-generation dataset: type: MT-bench name: MT-bench metrics: - type: GPT-4 as Judge value: 8.21 verified: false tags: - function-calling - tool-calling - agentic - rubra - conversational language: - en

Rubra Phi-3 Mini 128k Instruct

Model description

The model is the result of further post-training microsoft/Phi-3-mini-128k-instruct. This model is designed for high performance in various instruction-following tasks and complex interactions, including multi-turn function calling and detailed conversations.

Model	Function Calling	MMLU	GPQA	GSM-8K	MATH	MT-bench	Win	Loss	Tie	Win Rate	Loss Rate	Adjusted Win Rate
Phi-3 Mini 128k Instruct (June)	-	69.36	27.01	83.7	32.92	8.02	21	72	67	0.13125	0.45000	0.340625
Rubra Enhanced Phi-3 Mini 128k Instruct (June)	70.00%	67.87	29.69	79.45	30.80	8.21	72	21	67	0.45000	0.13125	0.659375
Phi-3 Mini 128k Instruct (April)	-	68.17	25.90	80.44	28.12	7.92	51	45	64	0.31875	0.28125	0.51875
Rubra Enhanced Phi-3 Mini 128k Instruct (April)	65.71%	66.66	29.24	74.09	26.84	7.45	45	51	64	0.28125	0.31875	0.48125

Commit e2ecb24bd9dae689bb30dafcf13cbbc9dbddead5 is the last commit to have the April-based Phi-3 model. The latest in main is built off the June model

Training Data

The model underwent additional training on a proprietary dataset encompassing diverse instruction-following, chat, and function calling data. This post-training process enhances the model's ability to integrate tools and manage complex interaction scenarios effectively.

How to use

You can use the model with the Hugging Face transformers and the rubra library rubra-tools as follows:

pip install rubra_tools torch==2.3.0 transformers accelerate

You also need Node.js and npm installed. Once you do, install the jsonrepair package - it's used to fix some rare hallucinations by the model.

npm install jsonrepair

1. Load the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from rubra_tools import preprocess_input, postprocess_output

model_id = "rubra-ai/Phi-3-mini-128k-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

2. Define Functions

Here we use 4 functions for a simple math chaining question:

functions = [
    {
            'type': 'function',
            'function': {
                'name': 'addition',
                'description': "Adds two numbers together",
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'a': {
                            'description': 'First number to add',
                            'type': 'string'
                        },
                        'b': {
                            'description': 'Second number to add',
                            'type': 'string'
                        }
                    },
                    'required': []
                }
            }
        },
        {
            'type': 'function',
            'function': {
                'name': 'subtraction',
                'description': "Subtracts two numbers",
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'a': {
                            'description': 'First number to be subtracted from',
                            'type': 'string'
                        },
                        'b': {
                            'description': 'Number to subtract',
                            'type': 'string'
                        }
                    },
                    'required': []
                }
            }
        },
        {
            'type': 'function',
            'function': {
                'name': 'multiplication',
                'description': "Multiply two numbers together",
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'a': {
                            'description': 'First number to multiply',
                            'type': 'string'
                        },
                        'b': {
                            'description': 'Second number to multiply',
                            'type': 'string'
                        }
                    },
                    'required': []
                }
            }
        },
        {
            'type': 'function',
            'function': {
                'name': 'division',
                'description': "Divide two numbers",
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'a': {
                            'description': 'First number to use as the dividend',
                            'type': 'string'
                        },
                        'b': {
                            'description': 'Second number to use as the divisor',
                            'type': 'string'
                        }
                    },
                    'required': []
                }
            }
        },
]

3. Start the conversation

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the result of four plus six? Take the result and add 2? Then multiply by 5 and then divide by two"},
]

def run_model(messages, functions):
    ## Format messages in Rubra's format
    formatted_msgs = preprocess_input(msgs=messages, tools=functions)

    input_ids = tokenizer.apply_chat_template(
        formatted_msgs,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)

    terminators = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("")
    ]

    outputs = model.generate(
        input_ids,
        max_new_tokens=1000,
        eos_token_id=terminators,
        do_sample=True,
        temperature=0.1,
        top_p=0.9,
    )
    response = outputs[0][input_ids.shape[-1]:]
    raw_output = tokenizer.decode(response, skip_special_tokens=True)
    return raw_output

raw_output = run_model(messages, functions)
# Check if there's a function call
function_call = postprocess_output(raw_output)
if function_call:
    print(function_call)
else:
    print(raw_output)

You should see this output, which is a function call made by the AI assistant:

[{'id': 'fc65a533', 'function': {'name': 'addition', 'arguments': '{"a": "4", "b": "6"}'}, 'type': 'function'}]

4. Add Executed Tool Result to Message History & Continue the Conversation

if function_call:
    # append the assistant tool call msg
    messages.append({"role": "assistant", "tool_calls": function_call})
    # append the result of the tool call in openai format, in this case, the value of add 6 to 4 is 10.
    messages.append({'role': 'tool', 'tool_call_id': function_call[0]["id"], 'name': function_call[0]["function"]["name"], 'content': '10'})
    raw_output = run_model(messages, functions)
    # Check if there's a function call
    function_call = postprocess_output(raw_output)
    if function_call:
        print(function_call)
    else:
        print(raw_output)

The LLM will make another call

[{'id': '2ffc3de4', 'function': {'name': 'addition', 'arguments': '{"a": "10", "b": "2"}'}, 'type': 'function'}]

Framework Versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1

Limitations and Bias

While the model performs well on a wide range of tasks, it may still produce biased or incorrect outputs. Users should exercise caution and critical judgment when using the model in sensitive or high-stakes applications. The model's outputs are influenced by the data it was trained on, which may contain inherent biases.

Ethical Considerations

Users should ensure that the deployment of this model adheres to ethical guidelines and consider the potential societal impact of the generated text. Misuse of the model for generating harmful or misleading content is strongly discouraged.

Acknowledgements

We would like to thank Microsoft for the model.

Contact Information

For questions or comments about the model, please reach out to the rubra team.

Citation

If you use this work, please cite it as:

@misc {rubra_ai_2024,
    author       = { Sanjay Nadhavajhala and Yingbei Tong },
    title        = { Phi-3-mini-128k-instruct },
    year         = 2024,
    url          = { https://huggingface.co/rubra-ai/Phi-3-mini-128k-instruct },
    doi          = { 10.57967/hf/2682 },
    publisher    = { Hugging Face }
}