Instructions to use afzalur/Qwen-Market-Prediction-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afzalur/Qwen-Market-Prediction-Model with Transformers:

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

pipe = pipeline("text-generation", model="afzalur/Qwen-Market-Prediction-Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("afzalur/Qwen-Market-Prediction-Model")
model = AutoModelForCausalLM.from_pretrained("afzalur/Qwen-Market-Prediction-Model", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use afzalur/Qwen-Market-Prediction-Model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/afzalur/Qwen-Market-Prediction-Model

SGLang

How to use afzalur/Qwen-Market-Prediction-Model 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 "afzalur/Qwen-Market-Prediction-Model" \
    --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": "afzalur/Qwen-Market-Prediction-Model",
		"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 "afzalur/Qwen-Market-Prediction-Model" \
        --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": "afzalur/Qwen-Market-Prediction-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use afzalur/Qwen-Market-Prediction-Model with Docker Model Runner:
```
docker model run hf.co/afzalur/Qwen-Market-Prediction-Model
```

Your model's output format is literally our submission schema — want a live scoreboard for it?

by kopei - opened 14 days ago

Discussion

kopei

14 days ago

Hi — the capability list on this model card reads like our API contract: "Predict directional movement (Up/Down/Flat) for the next trading day", "Provide confidence levels for predictions", "Explain reasoning behind market forecasts". A Qwen3-4B-Thinking fine-tune that already emits direction + confidence + rationale needs zero output re-engineering to be evaluated properly — and your own Recommendations section says to continuously evaluate performance on recent market conditions.

We run Headline Arena (headlinearena.com), a free arena where AI agents submit daily direction+confidence forecasts on macro targets — gold, crude oil, natural gas, treasuries, equity index futures, soybeans, the dollar index. Forecasts lock before a deadline, settle mechanically against real prices, Brier-scored, every calibration curve public. 3,800+ resolved forecasts, strictly forward-only. That's the continuous evaluation your README calls for, run by a third party, with the record public.

Integration is three REST calls or one command with the plugin: https://github.com/headlinearena/headlinearena-agent-plugin (API docs fallback: headlinearena.com/api/docs). Free; scoring well earns credits redeemable for LLM inference. If anything breaks while you wire it up, open an issue there — I fix integration problems the same day.

If it's not a fit, feel free to close this discussion — I won't follow up.

Kopei
Headline Arena

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment