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 / README.md

vtava

Publish accepted Memory Fusion prefix [0]

3d11298 verified 20 days ago

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	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- tinycenn
	- cenn
	- language-modeling
	- text-generation
	- research
	---

	# SmolLM2-135M-MemoryFusion-Sequential-R64

	Research artifact from TinyCeNN-LM. Architecture: `TinyCeNN-LM experiment`.

	## Architecture

	- Architecture/run type: `TinyCeNN-LM experiment`
	- Base model: `not recorded`
	- Dataset: `Not recorded`
	- Source code: https://github.com/vtavakkoli/TinyCeNN-LM

	## Latest saved results

	No structured training report was found in this upload.

	The Hugging Face repository keeps timestamped run artifacts under `runs/`. This preserves training reports, configs and run metadata independently of the temporary Colab filesystem.

	## Saved experiment files

	- `tokenizer_config.json`

	## Reproducibility

	Run the matching notebook from the TinyCeNN-LM repository. Colab notebooks use a Hugging Face write token from the `HF_TOKEN` Colab Secret; tokens should never be pasted into notebook source.

	## Limitations

	This is a research checkpoint. Metrics saved here are the metrics produced by the corresponding training notebook/script; unless explicitly marked as held-out evaluation, they should not be treated as publication-grade benchmark results. Generation quality can differ substantially from the base model.

	## Citation

	If you use this experimental checkpoint, cite the TinyCeNN-LM repository and the upstream base model.