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_progress.json

vtava

Publish accepted Memory Fusion prefix [0]

3d11298 verified 19 days ago

Raw History Blame Contribute Delete

1.57 kB

	{
	"format_version": 1,
	"stage": "accepted_layer_0",
	"accepted_layers": [
	0
	],
	"config": {
	"feature_dim": 32,
	"memory_rank": 64,
	"dilations": [
	1,
	2,
	4,
	8,
	16,
	32,
	64,
	128
	],
	"shifted_window": 8,
	"train_output_projection": true
	},
	"layer_reports": [
	{
	"layer": 0,
	"accepted": false,
	"steps": 275,
	"nmse": 0.048099592328071594,
	"cosine": 0.9809460043907166,
	"probe_nll": 2.957142174243927,
	"incremental_delta_nll": 0.04405015707015991,
	"cumulative_delta_nll": 0.04405015707015991,
	"round": 1
	},
	{
	"layer": 0,
	"accepted": false,
	"steps": 300,
	"nmse": 0.02023264765739441,
	"cosine": 0.991240918636322,
	"probe_nll": 2.9379348754882812,
	"incremental_delta_nll": 0.02484285831451416,
	"cumulative_delta_nll": 0.02484285831451416,
	"round": 2
	},
	{
	"layer": 0,
	"accepted": false,
	"steps": 275,
	"nmse": 0.01509437058120966,
	"cosine": 0.9934894442558289,
	"probe_nll": 2.933886468410492,
	"incremental_delta_nll": 0.020794451236724854,
	"cumulative_delta_nll": 0.020794451236724854,
	"round": 3
	},
	{
	"layer": 0,
	"accepted": true,
	"steps": 125,
	"nmse": 0.018665021285414696,
	"cosine": 0.9919254779815674,
	"probe_nll": 2.9270507097244263,
	"incremental_delta_nll": 0.01395869255065918,
	"cumulative_delta_nll": 0.01395869255065918,
	"round": 4
	}
	]
	}