Instructions to use nikhilchandak/OpenForecaster-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nikhilchandak/OpenForecaster-8B with Transformers:

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

pipe = pipeline("text-generation", model="nikhilchandak/OpenForecaster-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nikhilchandak/OpenForecaster-8B")
model = AutoModelForCausalLM.from_pretrained("nikhilchandak/OpenForecaster-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nikhilchandak/OpenForecaster-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nikhilchandak/OpenForecaster-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nikhilchandak/OpenForecaster-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nikhilchandak/OpenForecaster-8B

SGLang

How to use nikhilchandak/OpenForecaster-8B 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 "nikhilchandak/OpenForecaster-8B" \
    --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": "nikhilchandak/OpenForecaster-8B",
		"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 "nikhilchandak/OpenForecaster-8B" \
        --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": "nikhilchandak/OpenForecaster-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nikhilchandak/OpenForecaster-8B with Docker Model Runner:
```
docker model run hf.co/nikhilchandak/OpenForecaster-8B
```

Add library_name, pipeline_tag and links to paper/code

by nielsr HF Staff - opened Jan 2

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+16

-16

Files changed (1) hide show

README.md +16 -16

README.md CHANGED Viewed

@@ -1,22 +1,28 @@
 ---
-license: mit
 base_model: Qwen/Qwen3-8B
-tags:
-  - forecasting
-  - reasoning
-  - question-answering
-  - reinforcement-learning
-  - calibration
 datasets:
-  - nikhilchandak/OpenForesight
 language:
-  - en
 ---
 # OpenForecaster-8B
 **OpenForecaster-8B** is a specialized language model for forecasting and predicting future events. This model is post-trained from **Qwen3-8B** using reinforcement learning on the [OpenForesight dataset](https://huggingface.co/datasets/nikhilchandak/OpenForesight).
 ## Model Description
 OpenForecaster-8B is designed to make calibrated predictions on open-ended questions about future events. The model has been trained to:
@@ -57,12 +63,6 @@ OpenForecaster-8B achieves competitive performance with much larger models like
 - **Better Calibration**: More reliable confidence estimates
 - **Enhanced Consistency**: Reduced logical violations in predictions
-## More Information
-For more details about the model, training process, and evaluation results, please visit our website:
-**🌐 [https://openforecaster.github.io](https://openforecaster.github.io)**
 ## Citation
 ```bibtex
@@ -79,4 +79,4 @@ This model is released under the MIT License.
 ## Contact
-For questions or issues, please visit our [website](https://openforecaster.github.io) or open an issue on the model repository.

 ---
 base_model: Qwen/Qwen3-8B
 datasets:
+- nikhilchandak/OpenForesight
 language:
+- en
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- forecasting
+- reasoning
+- question-answering
+- reinforcement-learning
+- calibration
 ---
 # OpenForecaster-8B
 **OpenForecaster-8B** is a specialized language model for forecasting and predicting future events. This model is post-trained from **Qwen3-8B** using reinforcement learning on the [OpenForesight dataset](https://huggingface.co/datasets/nikhilchandak/OpenForesight).
+It was introduced in the paper [Scaling Open-Ended Reasoning to Predict the Future](https://huggingface.co/papers/2512.25070).
+**🌐 [Website](https://openforecaster.github.io) | 📄 [Paper](https://huggingface.co/papers/2512.25070) | 💻 [Code](https://github.com/OpenForecaster/scaling-forecasting-training)**
 ## Model Description
 OpenForecaster-8B is designed to make calibrated predictions on open-ended questions about future events. The model has been trained to:
 - **Better Calibration**: More reliable confidence estimates
 - **Enhanced Consistency**: Reduced logical violations in predictions
 ## Citation
 ```bibtex
 ## Contact
+For questions or issues, please visit our [website](https://openforecaster.github.io) or open an issue on the model repository.