Instructions to use Ting-Ting/stress_merged_02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ting-Ting/stress_merged_02 with Transformers:

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

pipe = pipeline("text-generation", model="Ting-Ting/stress_merged_02")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Ting-Ting/stress_merged_02")
model = AutoModelForMultimodalLM.from_pretrained("Ting-Ting/stress_merged_02")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ting-Ting/stress_merged_02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ting-Ting/stress_merged_02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ting-Ting/stress_merged_02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ting-Ting/stress_merged_02

SGLang

How to use Ting-Ting/stress_merged_02 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 "Ting-Ting/stress_merged_02" \
    --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": "Ting-Ting/stress_merged_02",
		"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 "Ting-Ting/stress_merged_02" \
        --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": "Ting-Ting/stress_merged_02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ting-Ting/stress_merged_02 with Docker Model Runner:
```
docker model run hf.co/Ting-Ting/stress_merged_02
```

Meta-Llama-3-8B Text Generation Model

This model is a text generation model based on Meta-Llama-3-8B.

Model Description

This model generates text based on a given prompt. It has been fine-tuned to generate jokes and other humorous content.

Usage

You can use this model for generating text with the following code:

from transformers import pipeline

# Initialize the pipeline with your model
generator = pipeline("text-generation", model="your-username/llama-joke-model")

# Generate text based on a prompt
prompt = "Generate a joke about Malaysia"
results = generator(prompt, max_length=100, num_return_sequences=1)

# Print the generated result
for result in results:
    print("Generated Joke:", result['generated_text'])

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

F32

Model tree for Ting-Ting/stress_merged_02

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1410)

this model