Instructions to use MemTensor/MemT-1.0-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MemTensor/MemT-1.0-Flash with Transformers:

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

pipe = pipeline("text-generation", model="MemTensor/MemT-1.0-Flash", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MemTensor/MemT-1.0-Flash", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MemTensor/MemT-1.0-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MemTensor/MemT-1.0-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MemTensor/MemT-1.0-Flash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MemTensor/MemT-1.0-Flash

SGLang

How to use MemTensor/MemT-1.0-Flash 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/MemT-1.0-Flash" \
    --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": "MemTensor/MemT-1.0-Flash",
		"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 "MemTensor/MemT-1.0-Flash" \
        --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": "MemTensor/MemT-1.0-Flash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MemTensor/MemT-1.0-Flash with Docker Model Runner:
```
docker model run hf.co/MemTensor/MemT-1.0-Flash
```

This work and KDA

by drzzzzzzzzz - opened 5 days ago

Discussion

drzzzzzzzzz

5 days ago

Both this work and KDA rely on the gated delta rule, with the main architectural difference here being two additional LSTM-style gates. To isolate the effect of the architecture from the retention post-training, have you benchmarked KDA under the exact same post-training framework?

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment