README.md

---
base_model: INSAIT-Institute/MamayLM-Gemma-3-4B-IT-v1.0
library_name: transformers
model_name: MamayLM-function-calling
tags:
- generated_from_trainer
- trl
- sft
licence: license
license: gemma
datasets:
- NousResearch/hermes-function-calling-v1
language:
- en
- uk
---

# Model Card for MamayLM-function-calling

This model is a fine-tuned version of [INSAIT-Institute/MamayLM-Gemma-3-4B-IT-v1.0](https://huggingface.co/INSAIT-Institute/MamayLM-Gemma-3-4B-IT-v1.0).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Evaluation
This is the first iteration of fine-tuning MamayLM for function calling. In the future, we plan to add metrics and improve training. <br>
During this phase new tokens (including tool_call) were introduced to the model and we evaluated how well it uses and understands the purpose of tool_call. <br>

### Metrics
- Accuracy in function calling (if response contains tool_call token) - ```find_longest_common_sequence_length(ground_truth_tokens, generated_tokens) / len(ground_truth_tokens)```
- Match in helpful exchange (if response does not contain tool_call token) - Computes the percentage of matching elements between generated tokens and ground truth tokens

### Performance before fine-tuning:
Accuracy in function calling: 0.38107 <br>
Match in helpful exchange: 0.07440 <br>

### Performance after fine-tuning:
Accuracy in function calling: 0.95415 <br>
Match in helpful exchange: 0.09937 <br>

## Quick start

```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "TymofiiNasobko/MamayLM-function-calling"
peftconfig = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
    peftconfig.base_model_name_or_path,
    attn_implementation="eager",
    device_map=device,
)
tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, peft_model_id)
model = model.to(compute_dtype)
model = model.eval()
```

## Training procedure
This model was trained with SFT.

### Framework versions

- TRL: 0.25.0
- Transformers: 4.57.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.4.1
- Tokenizers: 0.22.1

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```