Instructions to use sammysun0711/aquilachat-7b-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sammysun0711/aquilachat-7b-hf with Transformers:

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

pipe = pipeline("text-generation", model="sammysun0711/aquilachat-7b-hf", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sammysun0711/aquilachat-7b-hf", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("sammysun0711/aquilachat-7b-hf", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sammysun0711/aquilachat-7b-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sammysun0711/aquilachat-7b-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sammysun0711/aquilachat-7b-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sammysun0711/aquilachat-7b-hf

SGLang

How to use sammysun0711/aquilachat-7b-hf 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 "sammysun0711/aquilachat-7b-hf" \
    --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": "sammysun0711/aquilachat-7b-hf",
		"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 "sammysun0711/aquilachat-7b-hf" \
        --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": "sammysun0711/aquilachat-7b-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sammysun0711/aquilachat-7b-hf with Docker Model Runner:
```
docker model run hf.co/sammysun0711/aquilachat-7b-hf
```

sammysun0711 commited on Jul 3, 2023

Commit

720543f

1 Parent(s): 4e0f1d3

Update README.md

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@@ -8,12 +8,12 @@ https://github.com/FlagAI-Open/FlagAI/tree/master/examples/Aquila/Aquila-chat
 Support Inference with AutoModelForCausalLM, ORTModelForCausalLM and OVModelForCausalLM
 ```python
-#!pip install transformers>=4.29.2
-#!pip install optimum>=1.8.7 optimum-intel[openvino]==1.9.1
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained('sammysun0711/aquilachat-7b-hf')
 model = AutoModelForCausalLM.from_pretrained('sammysun0711/aquilachat-7b-hf', trust_remote_code=True)
 model = model.eval()
 # from optimum.onnxruntime import ORTModelForCausalLM

 Support Inference with AutoModelForCausalLM, ORTModelForCausalLM and OVModelForCausalLM
 ```python
+#!pip install transformers>=4.30.2
+#!pip install optimum>=1.8.8 optimum-intel[openvino]==1.9.1
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained('sammysun0711/aquilachat-7b-hf', trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained('sammysun0711/aquilachat-7b-hf', trust_remote_code=True)
 model = model.eval()
 # from optimum.onnxruntime import ORTModelForCausalLM