Instructions to use zw1429/wb_ds_interview_fns with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zw1429/wb_ds_interview_fns with Transformers:

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

pipe = pipeline("text-generation", model="zw1429/wb_ds_interview_fns")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zw1429/wb_ds_interview_fns")
model = AutoModelForCausalLM.from_pretrained("zw1429/wb_ds_interview_fns", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zw1429/wb_ds_interview_fns with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zw1429/wb_ds_interview_fns"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zw1429/wb_ds_interview_fns",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zw1429/wb_ds_interview_fns

SGLang

How to use zw1429/wb_ds_interview_fns 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 "zw1429/wb_ds_interview_fns" \
    --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": "zw1429/wb_ds_interview_fns",
		"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 "zw1429/wb_ds_interview_fns" \
        --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": "zw1429/wb_ds_interview_fns",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zw1429/wb_ds_interview_fns with Docker Model Runner:
```
docker model run hf.co/zw1429/wb_ds_interview_fns
```

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Check out the documentation for more information.

This model is fine-tuned on OpenAI's GPT2 and 42 World Bank Group documents, including various types of project assessments related to Food Nutrition and Security in Africa for the year 2018. The aim is to produce the "results narratives" for Scorecard outcome area 7 – Sustainable Food Systems.

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Safetensors

Model size

0.1B params

Tensor type

F32