Instructions to use niclasfw/SmolLM3-3B-llm-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use niclasfw/SmolLM3-3B-llm-code with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base")
model = PeftModel.from_pretrained(base_model, "niclasfw/SmolLM3-3B-llm-code")

Transformers

How to use niclasfw/SmolLM3-3B-llm-code with Transformers:

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

pipe = pipeline("text-generation", model="niclasfw/SmolLM3-3B-llm-code")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("niclasfw/SmolLM3-3B-llm-code", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use niclasfw/SmolLM3-3B-llm-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "niclasfw/SmolLM3-3B-llm-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "niclasfw/SmolLM3-3B-llm-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/niclasfw/SmolLM3-3B-llm-code

SGLang

How to use niclasfw/SmolLM3-3B-llm-code 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 "niclasfw/SmolLM3-3B-llm-code" \
    --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": "niclasfw/SmolLM3-3B-llm-code",
		"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 "niclasfw/SmolLM3-3B-llm-code" \
        --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": "niclasfw/SmolLM3-3B-llm-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use niclasfw/SmolLM3-3B-llm-code with Docker Model Runner:
```
docker model run hf.co/niclasfw/SmolLM3-3B-llm-code
```

niclasfw commited on Feb 4

Commit

7990743

verified ·

1 Parent(s): 9e1169d

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README.md +12 -4

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@@ -1,6 +1,11 @@
 ---
-library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
@@ -15,7 +20,7 @@ tags: []
 <!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
@@ -196,4 +201,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Model Card Contact
-[More Information Needed]

 ---
+base_model: HuggingFaceTB/SmolLM3-3B-Base
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:HuggingFaceTB/SmolLM3-3B-Base
+- lora
+- transformers
 ---
 # Model Card for Model ID
 <!-- Provide a longer summary of what this model is. -->
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 ## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.18.1