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Model: artindnr/tea Source: Original Platform
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README.md
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README.md
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---
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license: mit
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base_model: microsoft/phi-4
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tags:
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- fine-tuned
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- full-fine-tune
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- text-generation
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- chat
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- question-answering
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- assistant
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- pytorch
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language:
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- fa
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- en
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- multilingual
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pipeline_tag: text-generation
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---
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# 🍵 Tea
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**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.
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## Model Details
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- **Base model:** [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) (14B parameters)
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- **Fine-tuning method:** Full fine-tune — all parameters updated, no LoRA/PEFT adapters
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- **Fine-tuned by:** [artindnr](https://huggingface.co/artindnr)
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- **License:** MIT
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- **Languages:** Farsi (primary conversational focus), English, and general multilingual support
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- **Model type:** Causal decoder-only chat/assistant language model
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## What's New
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tea takes Phi-4's strong base reasoning and language capabilities and tunes them specifically for:
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- **Question answering** — direct, accurate answers grounded in the conversation context
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- **Long assistant conversations** — maintaining coherence, tone, and context over extended multi-turn sessions rather than short single-shot exchanges
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- **Farsi fluency** — natural, idiomatic Persian conversation and assistance, alongside solid English and multilingual performance
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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.
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## How to Use
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Tea uses the standard chat template shipped with the base model, so it works out of the box with 🤗 Transformers.
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### Generation
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_ID = "artindnr/tea"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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USER_PROMPT = "تو کی هستی و اسمت چیه؟"
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messages = [
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{"role": "user", "content": USER_PROMPT},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.7,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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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.
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## Intended Use
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tea is intended for:
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- Farsi-first conversational assistants that also need to handle English/multilingual input
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- Question-answering applications requiring direct, grounded answers
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- Long-running, multi-turn assistant deployments (support bots, tutoring, general-purpose chat) where conversational memory and coherence over many turns matters
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- Research comparing full fine-tunes vs. adapter-based (LoRA) fine-tunes on the same base model
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## Limitations
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- 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.
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- tea inherits the general capabilities and limitations of the `phi-4` base model, including the possibility of hallucinated facts, especially over very long contexts.
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- No formal safety fine-tuning beyond what is inherited from the base model has been applied; use appropriate safeguards in production settings.
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## License
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This model is released under the [MIT License](https://opensource.org/licenses/MIT), consistent with the base `microsoft/phi-4` model.
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## Citation
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If you use tea in your work, please cite:
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```bibtex
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@misc{tea,
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title = {tea: A Farsi-Focused, Full Fine-tune of Phi-4 for QA and Long-form Assistance},
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author = {artindnr},
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year = {2026},
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url = {https://huggingface.co/artindnr/tea}
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}
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```
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## Acknowledgements
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Built on top of [`microsoft/phi-4`](https://huggingface.co/microsoft/phi-4).
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