Instructions to use Wrooms/SMAXI-Community-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wrooms/SMAXI-Community-135M with Transformers:

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

pipe = pipeline("text-generation", model="Wrooms/SMAXI-Community-135M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wrooms/SMAXI-Community-135M")
model = AutoModelForCausalLM.from_pretrained("Wrooms/SMAXI-Community-135M", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Wrooms/SMAXI-Community-135M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wrooms/SMAXI-Community-135M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wrooms/SMAXI-Community-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Wrooms/SMAXI-Community-135M

SGLang

How to use Wrooms/SMAXI-Community-135M 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 "Wrooms/SMAXI-Community-135M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wrooms/SMAXI-Community-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Wrooms/SMAXI-Community-135M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wrooms/SMAXI-Community-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Wrooms/SMAXI-Community-135M with Docker Model Runner:
```
docker model run hf.co/Wrooms/SMAXI-Community-135M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SMAXI Community 135M

Experimental small model for extracting task, owner, due, and status fields from short fictional text. Independently fine-tuned from SmolLM2-135M-Instruct. It is not the proprietary SMAXI flagship and contains no flagship weights, private prompts, project data, or application source.

Measured evaluation

48 frozen fictional examples; task/owner values differ from training, but the template is shared. Greedy decoding, maximum 128 output tokens. This narrow test is not a general intelligence, coding, or safety benchmark. The upstream base is a control, not proprietary SMAXI.

Measure	Upstream base	Community
Exact JSON object match	4.2%	93.8%
Valid JSON object	66.7%	100.0%
Correct fields	4.2%	98.4%
Median seconds / example	0.661	0.336

Device: mps. Timing includes generation and device synchronization, excludes model loading, and is hardware-specific. There is no comparable flagship capability measurement. SMAXI's separate 12/12 software regression result is not a model score.

Training and provenance

Base revision: 12fd25f77366fa6b3b4b768ec3050bf629380bac
Parameters: 134,515,008
Full supervised fine-tune: 256 fictional examples, 80 optimizer steps, batch 4, learning rate 0.00002, seed 42.
Weight SHA-256: 9082ba37e3f2f088eadea5987582339c5e6c27e72af84a1becf583a635eed934
Dataset SHA-256: b429fe16da5ee6eb5db697986267d3d488f1b44f3fb6ba073ee820ccd18843e7
Test SHA-256: 2f2aadc8c37923b9b03c540db122f6c9cff46f3d33c4699d0316529fb11392fe
Actual parameter change verified. No private data or flagship weights used.

This model has no browsing, tools, memory, autonomous execution, privileged APIs, or connection to proprietary SMAXI. It retains capabilities from the public base and can produce incorrect outputs. Open weights can be modified or retrained; limits in the reference runner are not permanent restrictions. Smaller size does not guarantee lower performance than the flagship on every task.

Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
name = "Wrooms/SMAXI-Community-135M"
tokenizer = AutoTokenizer.from_pretrained(name, trust_remote_code=False)
model = AutoModelForCausalLM.from_pretrained(name, trust_remote_code=False).eval()
messages = [
    {"role": "system", "content": "You are SMAXI Community, a small text-only assistant. Extract task fields into JSON. You have no tools, browsing, private project access, or flagship connection."},
    {"role": "user", "content": "Extract task, owner, due, and status as JSON only. Task: label a folder; Owner: Iris; Due: 2027-02-01; Status: todo."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False)
if inputs["input_ids"].shape[1] > 1024:
    raise ValueError("Reference runner input limit is 1024 tokens")
with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=128, do_sample=False, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Apache-2.0; see LICENSE and NOTICE for upstream attribution and modifications. This repository distributes downloadable weights. Hosted inference availability is separate.

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Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for Wrooms/SMAXI-Community-135M

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Finetuned

(390)

this model

Quantizations

1 model