Instructions to use master103525/roun1-traing-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use master103525/roun1-traing-final with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "master103525/roun1-traing-final")

Transformers

How to use master103525/roun1-traing-final with Transformers:

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

pipe = pipeline("text-generation", model="master103525/roun1-traing-final")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("master103525/roun1-traing-final", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use master103525/roun1-traing-final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "master103525/roun1-traing-final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "master103525/roun1-traing-final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/master103525/roun1-traing-final

SGLang

How to use master103525/roun1-traing-final 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 "master103525/roun1-traing-final" \
    --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": "master103525/roun1-traing-final",
		"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 "master103525/roun1-traing-final" \
        --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": "master103525/roun1-traing-final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use master103525/roun1-traing-final with Docker Model Runner:
```
docker model run hf.co/master103525/roun1-traing-final
```

roun1-traing-final / training_args.bin

master103525

Upload folder using huggingface_hub

4be8e89 verified 27 days ago

Download

History Blame Contribute Delete

6.48 kB

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:: 5375ad5a4e79263955b1f96eac3d8f6c7c849869a7c10db2b3c5c682c58d4d1a
Size of remote file:: 6.48 kB
SHA256:: c72ec5a57d3be5d0f73afb47669bca1bfe0f79830d78f6ca7500c0efc61c92f1

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