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

Libraries

How to use ICTNLP/UMA-LedgerQA-4B with Transformers:

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

pipe = pipeline("text-generation", model="ICTNLP/UMA-LedgerQA-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ICTNLP/UMA-LedgerQA-4B")
model = AutoModelForCausalLM.from_pretrained("ICTNLP/UMA-LedgerQA-4B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ICTNLP/UMA-LedgerQA-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ICTNLP/UMA-LedgerQA-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": "ICTNLP/UMA-LedgerQA-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ICTNLP/UMA-LedgerQA-4B

SGLang

How to use ICTNLP/UMA-LedgerQA-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 "ICTNLP/UMA-LedgerQA-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": "ICTNLP/UMA-LedgerQA-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 "ICTNLP/UMA-LedgerQA-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": "ICTNLP/UMA-LedgerQA-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ICTNLP/UMA-LedgerQA-4B with Docker Model Runner:
```
docker model run hf.co/ICTNLP/UMA-LedgerQA-4B
```

UMA-LedgerQA-4B / README.md

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
base_model:
  - Qwen/Qwen3-4B-Instruct-2507
base_model_relation: finetune
tags:
  - memory-agent
  - reinforcement-learning
  - long-context
  - tool-use
  - state-tracking
  - ledger-qa
  - qwen3
  - grpo
arxiv: 2602.18493

UMA-LedgerQA-4B (Specialist)

UMA-LedgerQA-4B is the Specialist checkpoint of the Unified Memory Agent (UMA) introduced in Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning.

UMA is a tool-using memory agent that incrementally maintains a compact core summary and a structured key-value Memory Bank. The same policy performs memory construction and downstream question answering through explicit memory and retrieval operations.

Code: github.com/ictnlp/unified-memory-agent
Paper: arXiv:2602.18493
Generalist checkpoint: ICTNLP/UMA-4B

Checkpoint Variant

This repository contains the Specialist UMA checkpoint adapted to Ledger-QA, the paper's controlled benchmark for persistent state tracking under accumulated updates, overwrites, and aggregation.

Property	Value
Base model	`Qwen/Qwen3-4B-Instruct-2507`
Parameters	4B
Weight format	BF16 Safetensors
Training method	End-to-end reinforcement learning with Task-Stratified GRPO
Specialization data	Ledger-QA
Reported default context budget	16K

The Generalist and Specialist checkpoints share the same UMA architecture and tool interface. Use this checkpoint to reproduce or extend the Ledger-QA Specialist setting; use ICTNLP/UMA-4B for the broader Generalist setting.

Intended Use

This checkpoint is intended for research on:

persistent state tracking over long input streams;
updates, overwrites, contradiction handling, and aggregation;
proactive structured memory construction;
memory maintenance with explicit tool calls;
evaluation and extension of the UMA framework and Ledger-QA.

The checkpoint is designed to run inside the UMA two-phase agent loop. A plain text-generation call loads the language model, but does not by itself instantiate the Memory Bank, retrieval tools, prompts, or memory-to-QA workflow.

Loading the Weights

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ICTNLP/UMA-LedgerQA-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

The model can also be served through an OpenAI-compatible inference server:

vllm serve ICTNLP/UMA-LedgerQA-4B \
  --max-model-len 16384 \
  --gpu-memory-utilization 0.8

For full memory-agent inference, including the Memory Bank, memory tools, embedding retrieval, prompts, Ledger-QA data preparation, and evaluation, follow the official repository.

Limitations

This checkpoint is specialized for Ledger-QA and should not be presented as the paper's Generalist checkpoint.
Ledger-QA is a controlled diagnostic benchmark rather than comprehensive real-world validation.
The checkpoint is a research model and may generate incorrect answers or perform incorrect memory updates.
Agent behavior depends on the UMA prompt templates, tool implementations, retrieval backend, chunking policy, and inference configuration.
Persistent-memory applications can involve sensitive information. Deployments should provide appropriate privacy controls, retention policies, and user oversight.
This checkpoint should not be used as the sole basis for high-stakes decisions.

Citation

@article{zhang2026learning,
  title   = {Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning},
  author  = {Zhang, Kehao and Gui, Shangtong and Yang, Sheng and Chen, Wei and Feng, Yang},
  journal = {arXiv preprint arXiv:2602.18493},
  year    = {2026}
}