Instructions to use chihoonlee10/T3Q-Merge-SOLAR12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chihoonlee10/T3Q-Merge-SOLAR12 with Transformers:

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

pipe = pipeline("text-generation", model="chihoonlee10/T3Q-Merge-SOLAR12")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chihoonlee10/T3Q-Merge-SOLAR12")
model = AutoModelForCausalLM.from_pretrained("chihoonlee10/T3Q-Merge-SOLAR12")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chihoonlee10/T3Q-Merge-SOLAR12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chihoonlee10/T3Q-Merge-SOLAR12"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chihoonlee10/T3Q-Merge-SOLAR12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chihoonlee10/T3Q-Merge-SOLAR12

SGLang

How to use chihoonlee10/T3Q-Merge-SOLAR12 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 "chihoonlee10/T3Q-Merge-SOLAR12" \
    --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": "chihoonlee10/T3Q-Merge-SOLAR12",
		"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 "chihoonlee10/T3Q-Merge-SOLAR12" \
        --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": "chihoonlee10/T3Q-Merge-SOLAR12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chihoonlee10/T3Q-Merge-SOLAR12 with Docker Model Runner:
```
docker model run hf.co/chihoonlee10/T3Q-Merge-SOLAR12
```

T3Q-Merge-SOLAR12 / model-00003-of-00009.safetensors

chihoonlee10

Upload LlamaForCausalLM

15236be verified about 2 years ago

download

history blame

5 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:: 7892ec6d02c0cdbdaa2b325a8280ac308c272c115c7142a2f77f0e256f992155
Size of remote file:: 5 GB
SHA256:: d64bbc74d1bab54a19eaa29ebfdec63bf69c683b779284eae1c237370a527be1

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