Instructions to use Lythri/Lythri-Tada-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lythri/Lythri-Tada-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Lythri/Lythri-Tada-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Lythri/Lythri-Tada-4B")
model = AutoModelForMultimodalLM.from_pretrained("Lythri/Lythri-Tada-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lythri/Lythri-Tada-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lythri/Lythri-Tada-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lythri/Lythri-Tada-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lythri/Lythri-Tada-4B

SGLang

How to use Lythri/Lythri-Tada-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Lythri/Lythri-Tada-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lythri/Lythri-Tada-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Lythri/Lythri-Tada-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lythri/Lythri-Tada-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lythri/Lythri-Tada-4B with Docker Model Runner:
```
docker model run hf.co/Lythri/Lythri-Tada-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lythri

Hugging Face | ModelScope | Technical Report

Introduction

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:

Stronger instruction following. Tada scores 39.19 on IFEval (prompt-level strict), surpassing even the Lythri-7B-A4B (31.05).
Better multi-turn conversations. Tada keeps track of earlier context and user requests more consistently over long chats, and carries the emotional thread from turn to turn.
System prompt support. Unlike Lythri-4B-A2B, Tada follows system prompts, so you can set a persona, tone or rules for your app directly in the system message.

Long-tail knowledge, such as graduate-level science questions, is slightly weaker than Lythri-4B-A2B, while overall performance improves substantially.

Writing Naturalness

EQ-Bench Slop Score

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).

General Benchmarks

Benchmark	Lythri-4B-A2B	Lythri-Tada-4B
Knowledge
MMLU	55.23	55.41
ARC-C	52.99	56.57
Reasoning
HellaSwag	72.80	74.23
General
SocialIQA	49.80	51.43
Science
GPQA Diamond	28.79	21.21
Math
GSM8K	28.81	27.45
Code / Instruction
HumanEval	28.66	24.39
IFEval	26.43	39.19

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.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
import 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))

Recommended Generation Config

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)

System Prompt (Optional)

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:

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."},
]

Compute

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.

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.

License

Lythri is built on Gemma 4 and is released under the Apache License 2.0.

Citation

@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}
}