Instructions to use APMIC/caigun-lora-model-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use APMIC/caigun-lora-model-33B with Transformers:

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

pipe = pipeline("text-generation", model="APMIC/caigun-lora-model-33B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("APMIC/caigun-lora-model-33B")
model = AutoModelForCausalLM.from_pretrained("APMIC/caigun-lora-model-33B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use APMIC/caigun-lora-model-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "APMIC/caigun-lora-model-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "APMIC/caigun-lora-model-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/APMIC/caigun-lora-model-33B

SGLang

How to use APMIC/caigun-lora-model-33B 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 "APMIC/caigun-lora-model-33B" \
    --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": "APMIC/caigun-lora-model-33B",
		"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 "APMIC/caigun-lora-model-33B" \
        --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": "APMIC/caigun-lora-model-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use APMIC/caigun-lora-model-33B with Docker Model Runner:
```
docker model run hf.co/APMIC/caigun-lora-model-33B
```

corrine1106 commited on Nov 17, 2023

Commit

43789c7

1 Parent(s): 8fa6e1c

Update README.md

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@@ -7,29 +7,32 @@ This is model finetuned on fake news detection.
 Model Details:
 Model Name: caigun-lora-model-33B
 Model Version: 1.0
 Date Created: 2023/11/17
 Model Overview:
-Intended Use:
 caigun-lora-model-33B is a LLM designed for various purpose.
-Training Data:
 fake news related dataset
-Model Architecture:
 It is based on LLaMA architecture.
-Training Procedure:
 [Stay tuned for updates]
-Model Performance:
 [Stay tuned for updates]
-Potential Risks:
 It's important to consider ethical implications related to the use of our model.
-Updates and Version History:
 Version 1.0: finetuned on fake news detection.

 Model Details:
 Model Name: caigun-lora-model-33B
 Model Version: 1.0
 Date Created: 2023/11/17
 Model Overview:
+Intended Use:
 caigun-lora-model-33B is a LLM designed for various purpose.
+Training Data:
 fake news related dataset
+Model Architecture:
 It is based on LLaMA architecture.
+Training Procedure:
 [Stay tuned for updates]
+Model Performance:
 [Stay tuned for updates]
+Potential Risks:
 It's important to consider ethical implications related to the use of our model.
+Updates and Version History:
 Version 1.0: finetuned on fake news detection.