Instructions to use Den4ikAI/ebany_researcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Den4ikAI/ebany_researcher with Transformers:

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

pipe = pipeline("text-generation", model="Den4ikAI/ebany_researcher")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Den4ikAI/ebany_researcher")
model = AutoModelForCausalLM.from_pretrained("Den4ikAI/ebany_researcher", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Den4ikAI/ebany_researcher with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Den4ikAI/ebany_researcher"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Den4ikAI/ebany_researcher",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Den4ikAI/ebany_researcher

SGLang

How to use Den4ikAI/ebany_researcher 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 "Den4ikAI/ebany_researcher" \
    --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": "Den4ikAI/ebany_researcher",
		"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 "Den4ikAI/ebany_researcher" \
        --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": "Den4ikAI/ebany_researcher",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Den4ikAI/ebany_researcher with Docker Model Runner:
```
docker model run hf.co/Den4ikAI/ebany_researcher
```

ebany_researcher / pytorch_model.bin

Den4ikAI

Upload 9 files

04db6c5 about 3 years ago

Download

History Blame Contribute Delete

551 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: c8f6487edf367cab884cb8ae34b632962f41c5915c695fe7d51f3d095e99a194
Size of remote file:: 551 MB
SHA256:: 7de55646b34a3dc4c4d02174cd2e81db5d16321f8ca076fad2310389a059c032

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.