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

nielsr

Jan 2

Hi! I'm Niels from the Hugging Face community science team. I've noticed this model repository is missing some metadata tags that help users find and use it more easily.

This PR:

Adds library_name: transformers to identify the compatible library and enable the usage widget.
Adds pipeline_tag: text-generation to correctly categorize the model.
Adds explicit links to the paper and the official GitHub repository in the model card for better accessibility.

Please review and merge if this looks good to you!

Add library_name, pipeline_tag and links to paper/code51db22a9

nikhilchandak changed pull request status to merged Jan 4

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