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Lythri
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Model Details
Model Provider
Lythri
Model Tree
google/gemma-4-E2B
Input Modalities
Output Modalities
Supported Functionality
Lythri-Tada-4B is part of the Lythri 2.0 family. It answers directly without a thinking phase, while reinforce model's emotional understanding.
Compared with the previous-generation Lythri-4B-A2B, Tada brings three main upgrades:
Long-tail knowledge, such as graduate-level science questions, is slightly weaker than Lythri-4B-A2B, while overall performance improves substantially.
On the EQ-Bench Slop Score, which measures how much a model relies on typical AI phrasing (lower is better), Lythri-Tada-4B scores 39.0 and writes more naturally than GPT-4o (47.7) and DeepSeek-R1 (67.9), far ahead of Qwen3-4B (62.2) and Gemma-3-4B (85.2).
All benchmarks are evaluated with their official standard settings and the chat template applied, reflecting real-world inference conditions. Lythri-4B-A2B is evaluated with thinking; Lythri-Tada-4B answers directly without thinking.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_path = "Lythri/Lythri-Tada-4B"tok = AutoTokenizer.from_pretrained(model_path)model = AutoModelForCausalLM.from_pretrained( model_path, dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "My friend just lost their job and seems really down. What should I say to them?"}]chat = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(chat, return_tensors="pt", add_special_tokens=False).to(model.device) with torch.inference_mode(): out = model.generate( **inputs, max_new_tokens=1024, do_sample=True, temperature=0.8, top_p=0.95, eos_token_id=[1, 106], ) print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))python
generation_config = { "temperature": 0.8, "top_p": 0.95, "top_k": 64, "max_new_tokens": 1024, "repetition_penalty": 1.05, "do_sample": True, "eos_token_id": [1, 106],} out = model.generate(**inputs, **generation_config)Lythri-Tada-4B supports system prompts, which Lythri-4B-A2B does not. It works well without one; to set a persona or rules for your app, add a system message:
python
messages = [ {"role": "system", "content": "You are Lythri, a warm and caring companion. Reply in the same language as the user."}, {"role": "user", "content": "I had a really rough day at work."},]Lythri-Tada-4B starts from the Gemma4-E2B PT and training on SFT with CoT, followed by instant-reply SFT with multi-turn companion conversations, and online preference optimization on only writing with responses judged by Gemma2-27B.
Lythri is built on Gemma 4 and is released under the Apache License 2.0.
bibtex
@techreport{li2026lythri, title = {Lythri Technical Report}, author = {Li, Jiawen}, year = {2026}, institution = {Zenodo}, doi = {10.5281/zenodo.23179311}, url = {https://doi.org/10.5281/zenodo.23179311}}The whole training process is self-funded. If you like our model, please click a free like — that means a lot to me as a student!
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_path = "Lythri/Lythri-Tada-4B"tok = AutoTokenizer.from_pretrained(model_path)model = AutoModelForCausalLM.from_pretrained( model_path, dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "My friend just lost their job and seems really down. What should I say to them?"}]chat = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(chat, return_tensors="pt", add_special_tokens=False).to(model.device) with torch.inference_mode(): out = model.generate( **inputs, max_new_tokens=1024, do_sample=True, temperature=0.8, top_p=0.95, eos_token_id=[1, 106], ) print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))generation_config = { "temperature": 0.8, "top_p": 0.95, "top_k": 64, "max_new_tokens": 1024, "repetition_penalty": 1.05, "do_sample": True, "eos_token_id": [1, 106],} out = model.generate(**inputs, **generation_config)messages = [ {"role": "system", "content": "You are Lythri, a warm and caring companion. Reply in the same language as the user."}, {"role": "user", "content": "I had a really rough day at work."},]@techreport{li2026lythri, title = {Lythri Technical Report}, author = {Li, Jiawen}, year = {2026}, institution = {Zenodo}, doi = {10.5281/zenodo.23179311}, url = {https://doi.org/10.5281/zenodo.23179311}}