Lythri

Introduction

Lythri is a family of on-device language models built for emotional companionship. Instead of chasing math and coding scores, Lythri is trained to understand how people feel and to hold natural, multi-turn conversations, while staying small enough to run locally on a laptop or phone.

Lythri Overview

Model	Total Params	Active Params	Base Model	GGUF
Lythri-7B-A4B	7.46B	4.5B	Gemma 4 E4B	Lythri-7B-A4B-GGUF
Lythri-4B-A2B	4.63B	2.3B	Gemma 4 E2B	Lythri-4B-A2B-GGUF

Emotional Intelligence (Preliminary)

Zero-shot results.

Model	GoEmotions (Macro F1)	EmoBench (Acc)	EQ-Bench (v2)
Gemma 4 E4B	8.00	31.39	43.19
Lythri-7B-A4B	31.54	46.83	49.41

Note: These are preliminary results from an internal evaluation that is not yet formal or complete, and they may differ slightly from the final numbers. For full details and authoritative results, including Lythri-4B-A2B, please refer to the technical report (coming soon).

General Benchmarks

Benchmark	Lythri-4B-A2B	Lythri-7B-A4B
Knowledge
MMLU	55.23	69.09
MMLU-Pro	24.42	38.17
ARC-E	81.40	83.42
ARC-C	52.99	60.58
Reasoning
PIQA	79.49	81.88
HellaSwag	72.80	78.29
WinoGrande	68.43	74.90
General
CommonsenseQA	65.52	77.07
SocialIQA	49.80	50.46
TruthfulQA MC2	46.16	49.66
Science
OpenBookQA	41.00	43.60
GPQA Diamond	28.79	27.78
Math
GSM8K	28.81	62.02
MATH	3.62	21.28
Reading
BoolQ	73.15	85.32
Code / Instruction
HumanEval	28.66	45.12
IFEval	26.43	31.05

All benchmarks are evaluated with their official standard settings and in generative mode with chat template applied, reflecting real-world inference conditions. Think-tag outputs from model are stripped before answer extraction.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "Lythri/Lythri-7B-A4B"  # or "Lythri/Lythri-4B-A2B"
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) + "<think>"
inputs = tok(chat, return_tensors="pt").to(model.device)

with torch.inference_mode():
    out = model.generate(
        **inputs,
        max_new_tokens=2048,
        do_sample=False,
        eos_token_id=[1, 106],
    )

print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Recommended Generation Config

generation_config = {
    "temperature": 0.95,
    "top_p": 0.9,
    "top_k": 64,
    "max_new_tokens": 2048,
    "repetition_penalty": 1.05,
    "do_sample": True,
    "eos_token_id": [1, 106],
}

out = model.generate(**inputs, **generation_config)

Compute

The full development of Lythri, including training and evaluation, used about 2,842 GPU hours on NVIDIA RTX 6000D GPUs.

Limitations

Lythri is optimized for conversation and emotional understanding, not for math, coding or complex reasoning.
Lythri is not a substitute for professional mental health support. If you or someone you know is in crisis, please contact local emergency services or a crisis helpline.
Like all language models, it can produce inaccurate or inappropriate content.