Instructions to use vtava/SmolLM2-135M-MemoryFusion-Sequential-R64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vtava/SmolLM2-135M-MemoryFusion-Sequential-R64 with Transformers:

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

pipe = pipeline("text-generation", model="vtava/SmolLM2-135M-MemoryFusion-Sequential-R64")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vtava/SmolLM2-135M-MemoryFusion-Sequential-R64", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vtava/SmolLM2-135M-MemoryFusion-Sequential-R64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vtava/SmolLM2-135M-MemoryFusion-Sequential-R64

SGLang

How to use vtava/SmolLM2-135M-MemoryFusion-Sequential-R64 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 "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64" \
    --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": "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64",
		"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 "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64" \
        --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": "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vtava/SmolLM2-135M-MemoryFusion-Sequential-R64 with Docker Model Runner:
```
docker model run hf.co/vtava/SmolLM2-135M-MemoryFusion-Sequential-R64
```

SmolLM2-135M-MemoryFusion-Sequential-R64 / sequential_run_status.json

vtava

Publish accepted Memory Fusion prefix [0]

3d11298 verified 19 days ago

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508 Bytes

	{
	"status": "current_layer_needs_more_training",
	"accepted_layers": [
	0
	],
	"current_layer": 1,
	"rounds_completed": 12,
	"last_report": {
	"layer": 1,
	"accepted": false,
	"steps": 300,
	"nmse": 0.04072427377104759,
	"cosine": 0.9802741408348083,
	"probe_nll": 2.9506284594535828,
	"incremental_delta_nll": 0.023577749729156494,
	"cumulative_delta_nll": 0.03668522834777832
	},
	"message": "No next Transformer layer was replaced. Rerun to continue this same layer."
	}