Instructions to use quantumaikr/falcon-180B-wizard_alpaca_dolly_orca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quantumaikr/falcon-180B-wizard_alpaca_dolly_orca with Transformers:

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

pipe = pipeline("text-generation", model="quantumaikr/falcon-180B-wizard_alpaca_dolly_orca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("quantumaikr/falcon-180B-wizard_alpaca_dolly_orca")
model = AutoModelForCausalLM.from_pretrained("quantumaikr/falcon-180B-wizard_alpaca_dolly_orca")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use quantumaikr/falcon-180B-wizard_alpaca_dolly_orca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/quantumaikr/falcon-180B-wizard_alpaca_dolly_orca

SGLang

How to use quantumaikr/falcon-180B-wizard_alpaca_dolly_orca 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 "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca" \
    --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": "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca",
		"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 "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca" \
        --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": "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use quantumaikr/falcon-180B-wizard_alpaca_dolly_orca with Docker Model Runner:
```
docker model run hf.co/quantumaikr/falcon-180B-wizard_alpaca_dolly_orca
```

falcon-180B-wizard_alpaca_dolly_orca / README.md

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metadata

datasets:
  - tiiuae/falcon-refinedweb
  - nRuaif/wizard_alpaca_dolly_orca
language:
  - en
  - de
  - es
  - fr
inference: false
license: unknown

🇰🇷 quantumaikr/falcon-180B-wizard_alpaca_dolly_orca

quantumaikr/falcon-180B-wizard_alpaca_dolly_orca is a 180B parameters causal decoder-only model built by quantumaikr based on Falcon-180B-chat

How to Get Started with the Model

To run inference with the model in full bfloat16 precision you need approximately 8xA100 80GB or equivalent.

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "quantumaikr/falcon-180B-wizard_alpaca_dolly_orca"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Contact

🇰🇷 www.quantumai.kr

🇰🇷 hi@quantumai.kr [초거대언어모델 기술도입 문의환영]