Instructions to use mrs83/Kurtis-SmolLM2-1.7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrs83/Kurtis-SmolLM2-1.7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="mrs83/Kurtis-SmolLM2-1.7B-Instruct")

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

tokenizer = AutoTokenizer.from_pretrained("mrs83/Kurtis-SmolLM2-1.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mrs83/Kurtis-SmolLM2-1.7B-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrs83/Kurtis-SmolLM2-1.7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrs83/Kurtis-SmolLM2-1.7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrs83/Kurtis-SmolLM2-1.7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrs83/Kurtis-SmolLM2-1.7B-Instruct

SGLang

How to use mrs83/Kurtis-SmolLM2-1.7B-Instruct 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 "mrs83/Kurtis-SmolLM2-1.7B-Instruct" \
    --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": "mrs83/Kurtis-SmolLM2-1.7B-Instruct",
		"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 "mrs83/Kurtis-SmolLM2-1.7B-Instruct" \
        --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": "mrs83/Kurtis-SmolLM2-1.7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrs83/Kurtis-SmolLM2-1.7B-Instruct with Docker Model Runner:
```
docker model run hf.co/mrs83/Kurtis-SmolLM2-1.7B-Instruct
```

mrs83 commited on Jun 19

Commit

4ddaa1a

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@@ -15,14 +15,12 @@ pipeline_tag: text-generation
 # Model Card for Kurtis-SmolLM2-1.7B-Instruct
-> ### ⚠️ EXPERIMENTAL MODEL NOTICE — NOT FOR PRODUCTION USE
-> **Model Status:** RESEARCH PROTOTYPE ONLY
 >
-> This repository contains an experimental model checkpoint designed strictly for academic evaluation and research purposes. It is **not a production-ready system**.
->
-> #### Critical Constraints:
-> * **No Production Deployment:** This model must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances.
-> * **No Liability:** This model is provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.
 This model has been fine-tuned using Kurtis, an experimental fine-tuning, inference and evaluation tool for Small Language Models.

 # Model Card for Kurtis-SmolLM2-1.7B-Instruct
+> [!WARNING]
+> This repository contains experimental models designed strictly for academic evaluation and research purposes.
 >
+> Critical Constraints:
+> * **No Production Deployment:** Experimental models must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances.
+> * **No Liability:** Experimental models are provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.
 This model has been fine-tuned using Kurtis, an experimental fine-tuning, inference and evaluation tool for Small Language Models.