Instructions to use Orkhan/llama-2-7b-absa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Orkhan/llama-2-7b-absa with Transformers:

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

pipe = pipeline("text-generation", model="Orkhan/llama-2-7b-absa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Orkhan/llama-2-7b-absa")
model = AutoModelForCausalLM.from_pretrained("Orkhan/llama-2-7b-absa", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Orkhan/llama-2-7b-absa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Orkhan/llama-2-7b-absa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Orkhan/llama-2-7b-absa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Orkhan/llama-2-7b-absa

SGLang

How to use Orkhan/llama-2-7b-absa 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 "Orkhan/llama-2-7b-absa" \
    --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": "Orkhan/llama-2-7b-absa",
		"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 "Orkhan/llama-2-7b-absa" \
        --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": "Orkhan/llama-2-7b-absa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Orkhan/llama-2-7b-absa with Docker Model Runner:
```
docker model run hf.co/Orkhan/llama-2-7b-absa
```

Orkhan commited on Apr 3, 2024

Commit

08d4ef2

verified ·

1 Parent(s): aa95bb7

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ This enhancement equips the model to adeptly identify aspects and accurately ana
 Its advantage over traditional Aspect-Based Sentiment Analysis models is you do not need to train a model with domain-specific labeled data as the llama-2-7b-absa model generalizes very well. However, you may need more computing power.
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/62b58935593a2c49da6b0f5a/z6Ssrjh0K2OSDIZUFVpP5.png)
 While inferencing, please note that the model has been trained on sentences, not on paragraphs.
 It fits T4-GPU-enabled free Google Colab Notebook.

 Its advantage over traditional Aspect-Based Sentiment Analysis models is you do not need to train a model with domain-specific labeled data as the llama-2-7b-absa model generalizes very well. However, you may need more computing power.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62b58935593a2c49da6b0f5a/G8wDb1I2cWDQf1uo5qfGE.png)
 While inferencing, please note that the model has been trained on sentences, not on paragraphs.
 It fits T4-GPU-enabled free Google Colab Notebook.