Instructions to use zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned with Transformers:

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

pipe = pipeline("text-generation", model="zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned

SGLang

How to use zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned 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 "zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned" \
    --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": "zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned",
		"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 "zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned" \
        --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": "zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned with Docker Model Runner:
```
docker model run hf.co/zeliang0426/MemAgent-PTE-Qwen2.5-7B-Aligned
```

MemAgent-PTE-Qwen2.5-7B-Aligned

This repository is an automated off-node backup of Qwen/Qwen2.5-7B-Instruct-derived MemAgent-PTE r131 local step 20; initialized from r107 step 210 with optimizer restart. The latest verified export corresponds to global step 20.

The checkpoint includes custom modeling code. Load it only after reviewing that code, and pass trust_remote_code=True to Transformers. The exported safetensor payload is stored in float32; select an appropriate lower-precision torch_dtype at load time when GPU memory is constrained.

This is a research checkpoint derived from the Apache-2.0 Qwen model named in the source label. It was trained for long-context memory behavior and has not received a general safety evaluation. Validate it for the intended task before deployment. The training and uploader code is maintained at https://github.com/ZhangAIPI/mem-agent-pte.

backup_manifest.json records the exact checkpoint step, source metadata, file sizes, and upload time. During training, main is replaced only after a durable local checkpoint has been validated. Intermediate history is squashed after verification so this repository remains a latest-checkpoint backup.

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Safetensors

Model size

8B params

Tensor type

F32