Instructions to use hflog/ibivibiv-collosus_120b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hflog/ibivibiv-collosus_120b with Transformers:

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

pipe = pipeline("text-generation", model="hflog/ibivibiv-collosus_120b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("hflog/ibivibiv-collosus_120b")
model = AutoModelForMultimodalLM.from_pretrained("hflog/ibivibiv-collosus_120b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hflog/ibivibiv-collosus_120b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hflog/ibivibiv-collosus_120b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hflog/ibivibiv-collosus_120b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hflog/ibivibiv-collosus_120b

SGLang

How to use hflog/ibivibiv-collosus_120b 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 "hflog/ibivibiv-collosus_120b" \
    --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": "hflog/ibivibiv-collosus_120b",
		"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 "hflog/ibivibiv-collosus_120b" \
        --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": "hflog/ibivibiv-collosus_120b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hflog/ibivibiv-collosus_120b with Docker Model Runner:
```
docker model run hf.co/hflog/ibivibiv-collosus_120b
```

ibivibiv-collosus_120b / model-00011-of-00104.safetensors

hflog

Duplicate from ibivibiv/collosus_120b

6d66fba verified about 2 years ago

Download

History Blame Contribute Delete

4.36 GB

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:: c6d520147543f50471a6818f844f8eefb69fb20d794c060fb3ba0919778b1ac1
Size of remote file:: 4.36 GB
SHA256:: c1707aaf88d8522128324ed7284abff355194d9a0ebbbc75b0b1a59326a412d1

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