Instructions to use tkhangg/Dual-Explain-2round-Q1_first with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tkhangg/Dual-Explain-2round-Q1_first with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("tkhangg0910/Merged-Dual-Explain-Stage_full-Q1_first")
model = PeftModel.from_pretrained(base_model, "tkhangg/Dual-Explain-2round-Q1_first")

Transformers

How to use tkhangg/Dual-Explain-2round-Q1_first with Transformers:

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

pipe = pipeline("text-generation", model="tkhangg/Dual-Explain-2round-Q1_first")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tkhangg/Dual-Explain-2round-Q1_first", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tkhangg/Dual-Explain-2round-Q1_first with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tkhangg/Dual-Explain-2round-Q1_first"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tkhangg/Dual-Explain-2round-Q1_first",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tkhangg/Dual-Explain-2round-Q1_first

SGLang

How to use tkhangg/Dual-Explain-2round-Q1_first 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 "tkhangg/Dual-Explain-2round-Q1_first" \
    --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": "tkhangg/Dual-Explain-2round-Q1_first",
		"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 "tkhangg/Dual-Explain-2round-Q1_first" \
        --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": "tkhangg/Dual-Explain-2round-Q1_first",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tkhangg/Dual-Explain-2round-Q1_first with Docker Model Runner:
```
docker model run hf.co/tkhangg/Dual-Explain-2round-Q1_first
```

Dual-Explain-2round-Q1_first

File size: 167 Bytes

e252c3c

{
  "custom_modules": [
    "lm_head",
    "box_encoder",
    "text_prompt_proj",
    "box_decoder",
    "model"
  ],
  "total_params": 1504,
  "saved_at_step": 2022
}