Instructions to use atenareply/smollm2-135m-noval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use atenareply/smollm2-135m-noval with Transformers:

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

pipe = pipeline("text-generation", model="atenareply/smollm2-135m-noval")

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

tokenizer = AutoTokenizer.from_pretrained("atenareply/smollm2-135m-noval")
model = AutoModelForCausalLM.from_pretrained("atenareply/smollm2-135m-noval", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use atenareply/smollm2-135m-noval with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "atenareply/smollm2-135m-noval"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "atenareply/smollm2-135m-noval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/atenareply/smollm2-135m-noval

SGLang

How to use atenareply/smollm2-135m-noval 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 "atenareply/smollm2-135m-noval" \
    --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": "atenareply/smollm2-135m-noval",
		"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 "atenareply/smollm2-135m-noval" \
        --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": "atenareply/smollm2-135m-noval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use atenareply/smollm2-135m-noval with Docker Model Runner:
```
docker model run hf.co/atenareply/smollm2-135m-noval
```

SmolLM2-135M Noval — Domain CPT

HuggingFaceTB/SmolLM2-135M after Continued Pre-Training on the fictional OMC + Mars Express domain corpus. First-round (135M) proof of the pipeline.

Overview

Stage: Continued Pre-Training (full fine-tune, CLM)
Lineage: SmolLM2-135M → CPT (this model)
Method: Continued Pre-Training (next-token, full fine-tune) on a 30/70 mars/orbital interleaved corpus (tokenized + packed).
Domain: fictional — Orbital Mining Corporation (OMC) technical docs + Mars Express telemetry.

Training


Corpus	noval-corp-training-corpus (30% Mars / 70% OMC, packed input_ids)
LR / schedule	5e-4 cosine, warmup 200 steps
Duration / seq	max_steps 5000, seq 1024, eff_batch 32, T4 fp16

Evaluation

No task metrics for this checkpoint (intermediate / no-training stage).

Intended use & limitations

Domain backbone for the 135M line (knows the domain; not instruction-tuned).

Limitations:

135M is intrinsically limited — expectations should be modest.
Fictional domain; no chat template / instruction following.

Card generated by noval-corp/scripts/gen_model_cards.py (standardized across the noval-corp model family).

Downloads last month: 23

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for atenareply/smollm2-135m-noval

Base model

HuggingFaceTB/SmolLM2-135M

Finetuned

(992)

this model

atenareply
/

smollm2-135m-noval