Instructions to use ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ethicalabs/Echo-DSRN-114M-v0.1.2")
model = PeftModel.from_pretrained(base_model, "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT")

Transformers

How to use ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT with Transformers:

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

pipe = pipeline("text-generation", model="ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT

SGLang

How to use ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT 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 "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT with Docker Model Runner:
```
docker model run hf.co/ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT
```

Model Card for ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT

The Echo-DSRN(N) (Dual State Recurrent Neural Network, short name: Echo-DSRN, also know as echo) is a novel architecture specifically designed to be a viable alternative for low-resource tasks ("smol" tasks) that are currently being inefficiently handled by the excessive scale of Large Language Models (LLMs) 🌱

This LoRA adapter has been fine-tuned by using the Flower Framework and TRL in a simulated federated learning scenario.

Model Usage

You can load the fine-tuned PEFT adapter over the base model and run sentiment analysis inference as follows:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the base model and tokenizer
base_model_name = "ethicalabs/Echo-DSRN-114M-v0.1.2"
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)

# Load the fine-tuned PEFT adapter
peft_model_name = "ethicalabs/FlowerTune-Echo-DSRN-114M-Finance-PEFT"
model = PeftModel.from_pretrained(base_model, peft_model_name, trust_remote_code=True)

# Prepare the prompt for sentiment analysis
instruction = "What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}."
news_text = "The company reported a 20% increase in revenue for the third quarter, driven by strong sales in the tech division."

messages = [
    {"role": "system", "content": "You are a helpful financial assistant."},
    {"role": "user", "content": f"{instruction} {news_text}"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10)
response = tokenizer.decode(outputs[0], skip_special_tokens=False)

print(response.split("<|assistant|>")[1].replace("<|end|>", "").strip() if "<|assistant|>" in response else response)

Training

Trained on a single AMD Radeon™ AI PRO R9700 (32 GB RAM).

You can download the source code and reproduce the training process by simply running the following commands:

# Downloads the flower app from Flower Hub
flwr new @mrs83/echo-dsrn-114m-finance

# Federation Local Setup
flwr federation simulation-config \
    --num-supernodes=10 \
    --client-resources-num-cpus=6 \
    --client-resources-num-gpus=1.0

# Runs the federation and streams training log output.
flwr run --stream

Source code is available on the Flower Hub

🏗️ Architecture Details

Property	Value
Model Type	echo_dsrn
Layers	8
Hidden Dim	512
Attention Heads	4
MLP Ratio	8.0
Vocab Size	32011
Hybrid Attention	True
RMSNorm	True

📊 Parameter Breakdown

Component	Parameters	% of Total
Total	114.69M (114,687,488)	100%
Embeddings	16.39M	14.29%
DSRN Blocks (Aggregate)	81.91M	71.42%
LM Head	16.39M	14.29%

🧩 Internal Block Structure (Per Layer)

Sub-Component	Parameters	Description
MLP (Feed-Forward)	4.20M	Upscaled hidden layers
DSRN Slow State	3.15M	Constant-time memory gates
GRU Fast State	1.58M	Recurrent fast path
Surprise Gating	264,192	Dynamic focus mechanism
Normalization	1,024	LayerNorm / RMSNorm

Evaluation

To evaluate the best federated checkpoint (e.g., round 38) across the financial benchmarks (FPB, FIQA, TFNS), use the integrated evaluation script:

python echo_finance/eval.py \
    --base-model-name-path ethicalabs/Echo-DSRN-114M-v0.1.2 \
    --peft-path ~/.ethicalabs/flwr/results/[RUN_TIMESTAMP]/peft_38 \
    --datasets fpb,fiqa,tfns \
    --apply-chat-template

Note: The --apply-chat-template flag ensures the model is evaluated accurately using the structural ChatML format it was aligned on.

Benchmarks

Dataset	Sentiment Accuracy
FPB (Financial PhraseBank)	71.45%
TFNS (Twitter Financial News Sentiment)	70.14%
FIQA (Financial QA)	63.82%

Citations

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

@misc{gao2025flowertune,
  title = {FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models},
  author = {Yan Gao and Massimo Roberto Scamarcia and Javier Fernandez-Marques and Mohammad Naseri and Chong Shen Ng and Dimitris Stripelis and Zexi Li and Tao Shen and Jiamu Bai and Daoyuan Chen and Zikai Zhang and Rui Hu and InSeo Song and Lee KangYoon and Hong Jia and Ting Dang and Junyan Wang and Zheyuan Liu and Daniel Janes Beutel and Lingjuan Lyu and Nicholas D. Lane},
  year = {2025},
  eprint = {2506.02961},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url = {https://arxiv.org/abs/2506.02961}
}

@misc{Massimo Roberto Scamarcia, title={Echo-DSRN-114M: Surprise-Gated Dual-State Recurrent Architecture for Efficient Language Modeling and Classification}, DOI={10.5281/zenodo.19848279}, publisher={Zenodo}, author={Massimo Roberto Scamarcia} }