Instructions to use LeviDeHaan/SmolNewsAnalysis-002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeviDeHaan/SmolNewsAnalysis-002 with Transformers:

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

pipe = pipeline("text-generation", model="LeviDeHaan/SmolNewsAnalysis-002")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeviDeHaan/SmolNewsAnalysis-002")
model = AutoModelForCausalLM.from_pretrained("LeviDeHaan/SmolNewsAnalysis-002", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LeviDeHaan/SmolNewsAnalysis-002 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeviDeHaan/SmolNewsAnalysis-002"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeviDeHaan/SmolNewsAnalysis-002",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeviDeHaan/SmolNewsAnalysis-002

SGLang

How to use LeviDeHaan/SmolNewsAnalysis-002 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 "LeviDeHaan/SmolNewsAnalysis-002" \
    --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": "LeviDeHaan/SmolNewsAnalysis-002",
		"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 "LeviDeHaan/SmolNewsAnalysis-002" \
        --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": "LeviDeHaan/SmolNewsAnalysis-002",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeviDeHaan/SmolNewsAnalysis-002 with Docker Model Runner:
```
docker model run hf.co/LeviDeHaan/SmolNewsAnalysis-002
```

LeviDeHaan commited on Oct 26, 2025

Commit

b358c84

verified ·

1 Parent(s): be6b3d6

Update README.md with YAML metadata

Browse files

Files changed (1) hide show

README.md +16 -2

README.md CHANGED Viewed

@@ -1,3 +1,15 @@
 # SmolLM2-360M Financial News JSON Analyst (`SmolNewsAnalysis-002`)
 - **Hugging Face model card**: https://huggingface.co/LeviDeHaan/SmolNewsAnalysis-002
@@ -59,7 +71,8 @@ model_id = "LeviDeHaan/SmolNewsAnalysis-002"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
-prompt = """<|im_start|>system\nYou are a precise financial news analyst...<|im_end|>\n"""
 prompt += "<|im_start|>user\nTesla shares climb after deliveries beat expectations. Symbol: TSLA Site: bloomberg.com\n<|im_end|>\n<|im_start|>assistant\n"
 inputs = tokenizer(prompt, return_tensors="pt")
@@ -94,4 +107,5 @@ print(response)
 ## Contact & Support
 - **Maintainer** [Levi De Haan](https://levidehaan.com/)
 - **Project page** https://levidehaan.com/projects/twatter
-- **Hugging Face discussions** https://huggingface.co/LeviDeHaan/SmolNewsAnalysis-002/discussions

+---
+language: en
+library_name: transformers
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-360M-Instruct
+pipeline_tag: text-generation
+tags:
+- text-generation
+- smollm
+- financial-news
+- json
+---
 # SmolLM2-360M Financial News JSON Analyst (`SmolNewsAnalysis-002`)
 - **Hugging Face model card**: https://huggingface.co/LeviDeHaan/SmolNewsAnalysis-002
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
+prompt = """<|im_start|>system
+You are a precise financial news analyst...<|im_end|>\n"""
 prompt += "<|im_start|>user\nTesla shares climb after deliveries beat expectations. Symbol: TSLA Site: bloomberg.com\n<|im_end|>\n<|im_start|>assistant\n"
 inputs = tokenizer(prompt, return_tensors="pt")
 ## Contact & Support
 - **Maintainer** [Levi De Haan](https://levidehaan.com/)
 - **Project page** https://levidehaan.com/projects/twatter
+- **Hugging Face discussions** https://huggingface.co/LeviDeHaan/SmolNewsAnalysis-002/discussions