Instructions to use chihoonlee10/T3Q-LLM-MG-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chihoonlee10/T3Q-LLM-MG-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="chihoonlee10/T3Q-LLM-MG-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chihoonlee10/T3Q-LLM-MG-v1.0")
model = AutoModelForCausalLM.from_pretrained("chihoonlee10/T3Q-LLM-MG-v1.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chihoonlee10/T3Q-LLM-MG-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chihoonlee10/T3Q-LLM-MG-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chihoonlee10/T3Q-LLM-MG-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chihoonlee10/T3Q-LLM-MG-v1.0

SGLang

How to use chihoonlee10/T3Q-LLM-MG-v1.0 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-LLM-MG-v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chihoonlee10/T3Q-LLM-MG-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-LLM-MG-v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chihoonlee10/T3Q-LLM-MG-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chihoonlee10/T3Q-LLM-MG-v1.0 with Docker Model Runner:
```
docker model run hf.co/chihoonlee10/T3Q-LLM-MG-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

T3Q-LLM-MG-v1.0

Model Developers Chihoon Lee(chihoonlee10), T3Q

Python code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

MODEL_DIR = "chihoonlee10/T3Q-LLM-MG-v1.0"
model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

s = "한국의 수도는 어디?"
conversation = [{'role': 'user', 'content': s}]
inputs = tokenizer.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors='pt').to("cuda")
_ = model.generate(inputs, streamer=streamer, max_new_tokens=1024)

hf (pretrained=chihoonlee10/T3Q-LLM-MG-v1.0), limit: None, provide_description: False, num_fewshot: 0, batch_size: None

Task	Version	Metric	Value		Stderr
kobest_boolq	0	acc	0.9523	±	0.0057
		macro_f1	0.9523	±	0.0057
kobest_copa	0	acc	0.7740	±	0.0132
		macro_f1	0.7737	±	0.0133
kobest_hellaswag	0	acc	0.4980	±	0.0224
		acc_norm	0.5920	±	0.0220
		macro_f1	0.4950	±	0.0223
kobest_sentineg	0	acc	0.7254	±	0.0224
		macro_f1	0.7106	±	0.0234

T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0

Task	Version	Metric	Value		Stderr
kobest_boolq	0	acc	0.9387	±	0.0064
		macro_f1	0.9387	±	0.0064
kobest_copa	0	acc	0.7590	±	0.0135
		macro_f1	0.7585	±	0.0135
kobest_hellaswag	0	acc	0.5080	±	0.0224
		acc_norm	0.5580	±	0.0222
		macro_f1	0.5049	±	0.0224
kobest_sentineg	0	acc	0.8489	±	0.0180
		macro_f1	0.8483	±	0.0180

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Safetensors

Model size

11B params

Tensor type

F32

Model tree for chihoonlee10/T3Q-LLM-MG-v1.0

Quantizations

2 models

chihoonlee10
/

T3Q-LLM-MG-v1.0

T3Q-LLM-MG-v1.0

Model Developers Chihoon Lee(chihoonlee10), T3Q

Python code

T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0

Model tree for chihoonlee10/T3Q-LLM-MG-v1.0

Dataset used to train chihoonlee10/T3Q-LLM-MG-v1.0

Space using chihoonlee10/T3Q-LLM-MG-v1.0 1