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

File size: 394 Bytes

3d11298

{
  "run_id": "hf_memory_fusion_export-20260915T220116Z",
  "created_utc": "2026-09-15T22:01:16.053571+00:00",
  "repo_id": "vtava/SmolLM2-135M-MemoryFusion-Sequential-R64",
  "notebook": null,
  "python": "3.13.15",
  "platform": "Linux-6.6.122+-x86_64-with-glibc2.39",
  "reports": [
    "tokenizer_config.json"
  ],
  "torch": "2.11.0+cu128",
  "cuda_available": true,
  "gpu": "NVIDIA L4"
}