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

Libraries

How to use MemTensor/MemOperator-4B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MemTensor/MemOperator-4B")
model = AutoModelForCausalLM.from_pretrained("MemTensor/MemOperator-4B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MemTensor/MemOperator-4B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MemTensor/MemOperator-4B

SGLang

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

Docker Model Runner
How to use MemTensor/MemOperator-4B with Docker Model Runner:
```
docker model run hf.co/MemTensor/MemOperator-4B
```

Nyakult commited on Jul 29, 2025

Commit

548fdba

verified ·

1 Parent(s): 9c7a62d

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -14

README.md CHANGED Viewed

@@ -171,7 +171,7 @@ print(doc_memory)
 Configure your mem_cube_config.json:
 ```Json
 {
-  "...": "...",
   "reorganize": true,
   "text_mem": {
     "backend": "tree_text",
@@ -187,25 +187,15 @@ Configure your mem_cube_config.json:
         }
       },
       "dispatcher_llm": {
-        "backend": "huggingface",
-        "config": {
-          "model_name_or_path": "Qwen/Qwen-1.8B-Chat",
-          "temperature": 0.7,
-          "max_tokens": 512
         }
       },
       "graph_db": {
-        "backend": "neo4j",
-        "config": {
-          "uri": "bolt://localhost:7687",
-          "username": "neo4j",
-          "password": "your_password"
         }
       },
       "embedder": {
-        "backend": "sentence_transformers",
-        "config": {
-          "model_name_or_path": "all-MiniLM-L6-v2"
         }
       }
     }

 Configure your mem_cube_config.json:
 ```Json
 {
+  ...,
   "reorganize": true,
   "text_mem": {
     "backend": "tree_text",
         }
       },
       "dispatcher_llm": {
+        ...
         }
       },
       "graph_db": {
+        ...
         }
       },
       "embedder": {
+        ...
         }
       }
     }