Instructions to use a753463/TW-ABSA-Split-8b-instruct-taide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use a753463/TW-ABSA-Split-8b-instruct-taide with Transformers:

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

pipe = pipeline("text-generation", model="a753463/TW-ABSA-Split-8b-instruct-taide")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("a753463/TW-ABSA-Split-8b-instruct-taide")
model = AutoModelForCausalLM.from_pretrained("a753463/TW-ABSA-Split-8b-instruct-taide", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use a753463/TW-ABSA-Split-8b-instruct-taide with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "a753463/TW-ABSA-Split-8b-instruct-taide"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "a753463/TW-ABSA-Split-8b-instruct-taide",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/a753463/TW-ABSA-Split-8b-instruct-taide

SGLang

How to use a753463/TW-ABSA-Split-8b-instruct-taide 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 "a753463/TW-ABSA-Split-8b-instruct-taide" \
    --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": "a753463/TW-ABSA-Split-8b-instruct-taide",
		"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 "a753463/TW-ABSA-Split-8b-instruct-taide" \
        --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": "a753463/TW-ABSA-Split-8b-instruct-taide",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use a753463/TW-ABSA-Split-8b-instruct-taide with Docker Model Runner:
```
docker model run hf.co/a753463/TW-ABSA-Split-8b-instruct-taide
```

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

Uploaded model

Developed by: a753463
License: apache-2.0
Finetuned from model : taide/Llama-3.1-TAIDE-LX-8B-Chat

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 11

Model tree for a753463/TW-ABSA-Split-8b-instruct-taide

Base model

taide/Llama-3.1-TAIDE-LX-8B-Chat

Finetuned

(28)

this model