Instructions to use Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged with Transformers:

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

pipe = pipeline("text-generation", model="Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged")

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

tokenizer = AutoTokenizer.from_pretrained("Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged")
model = AutoModelForCausalLM.from_pretrained("Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged

SGLang

How to use Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged 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 "Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged" \
    --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": "Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged",
		"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 "Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged" \
        --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": "Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged with Docker Model Runner:
```
docker model run hf.co/Nishef/SmolLM2-360M-Full_KTO_20251225_020028-merged
```

Nishef commited on Dec 26, 2025

Commit

fec3128

verified ·

1 Parent(s): cc782c4

Add training loss curve plot

Browse files

Files changed (2) hide show

.gitattributes +1 -0
thesis_plots/training_loss.png +3 -0

.gitattributes CHANGED Viewed

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 thesis_plots/benchmark_results.png filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 thesis_plots/benchmark_results.png filter=lfs diff=lfs merge=lfs -text
+thesis_plots/training_loss.png filter=lfs diff=lfs merge=lfs -text

thesis_plots/training_loss.png ADDED Viewed

Git LFS Details

SHA256: 0c0807ec5db8c841e2239106f19b76e00f9901d62bbfe2f05c880301cf93e9e7
Pointer size: 131 Bytes
Size of remote file: 245 kB