README.md

---
license: mit
base_model: microsoft/phi-4
tags:
- fine-tuned
- full-fine-tune
- text-generation
- chat
- question-answering
- assistant
- pytorch
language:
  - fa
  - en
  - multilingual
pipeline_tag: text-generation
---

# 🍵 Tea
![https://64.media.tumblr.com/1ab2cfe03429ef47b5063c90df2c0a5a/3c1a235d6f992b74-7d/s500x750/aafa42b7949f3104f349e5508df5fa8b738d879a.gif](https://64.media.tumblr.com/1ab2cfe03429ef47b5063c90df2c0a5a/3c1a235d6f992b74-7d/s500x750/aafa42b7949f3104f349e5508df5fa8b738d879a.gif)

**Tea** is a full fine-tune of [`microsoft/phi-4`](https://huggingface.co/microsoft/phi-4), built for **question answering and long, sustained assistant-style conversations**. It is multilingual, with fine-tuning focused on strong, natural **Farsi (Persian)** conversational ability, while retaining Phi-4's general English and multilingual competence.

## Model Details

- **Base model:** [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) (14B parameters)
- **Fine-tuning method:** Full fine-tune — all parameters updated, no LoRA/PEFT adapters
- **Fine-tuned by:** [artindnr](https://huggingface.co/artindnr)
- **License:** MIT
- **Languages:** Farsi (primary conversational focus), English, and general multilingual support
- **Model type:** Causal decoder-only chat/assistant language model

## What's New

tea takes Phi-4's strong base reasoning and language capabilities and tunes them specifically for:

- **Question answering** — direct, accurate answers grounded in the conversation context
- **Long assistant conversations** — maintaining coherence, tone, and context over extended multi-turn sessions rather than short single-shot exchanges
- **Farsi fluency** — natural, idiomatic Persian conversation and assistance, alongside solid English and multilingual performance

Unlike adapter-based fine-tunes, every weight in the model was updated during training, which the author has found gives more consistent behavior for long-conversation use cases than LoRA-based approaches.

## How to Use

Tea uses the standard chat template shipped with the base model, so it works out of the box with 🤗 Transformers.

### Generation

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "artindnr/tea"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

USER_PROMPT = "تو کی هستی و اسمت چیه؟"

messages = [
    {"role": "user", "content": USER_PROMPT},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    do_sample=True,
)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

For multi-turn conversations, simply keep appending `{"role": "user", ...}` / `{"role": "assistant", ...}` turns to the `messages` list before re-applying the chat template — tea is tuned to stay coherent as this history grows.

## Intended Use

tea is intended for:

- Farsi-first conversational assistants that also need to handle English/multilingual input
- Question-answering applications requiring direct, grounded answers
- Long-running, multi-turn assistant deployments (support bots, tutoring, general-purpose chat) where conversational memory and coherence over many turns matters
- Research comparing full fine-tunes vs. adapter-based (LoRA) fine-tunes on the same base model

## Limitations

- As a full fine-tune, tea's Farsi-focused training may shift some of Phi-4's original English-centric behaviors; for English-only, general-purpose use cases the base `microsoft/phi-4` model may still be preferable.
- tea inherits the general capabilities and limitations of the `phi-4` base model, including the possibility of hallucinated facts, especially over very long contexts.
- No formal safety fine-tuning beyond what is inherited from the base model has been applied; use appropriate safeguards in production settings.

## License

This model is released under the [MIT License](https://opensource.org/licenses/MIT), consistent with the base `microsoft/phi-4` model.

## Citation

If you use tea in your work, please cite:

```bibtex
@misc{tea,
  title  = {tea: A Farsi-Focused, Full Fine-tune of Phi-4 for QA and Long-form Assistance},
  author = {artindnr},
  year   = {2026},
  url    = {https://huggingface.co/artindnr/tea}
}
```

## Acknowledgements

Built on top of [`microsoft/phi-4`](https://huggingface.co/microsoft/phi-4).