Instructions to use Miguelpef/bart-base-lora-3DPrompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Miguelpef/bart-base-lora-3DPrompt with Transformers:

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

pipe = pipeline("text-generation", model="Miguelpef/bart-base-lora-3DPrompt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Miguelpef/bart-base-lora-3DPrompt")
model = AutoModelForSeq2SeqLM.from_pretrained("Miguelpef/bart-base-lora-3DPrompt")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Miguelpef/bart-base-lora-3DPrompt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Miguelpef/bart-base-lora-3DPrompt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Miguelpef/bart-base-lora-3DPrompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Miguelpef/bart-base-lora-3DPrompt

SGLang

How to use Miguelpef/bart-base-lora-3DPrompt 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 "Miguelpef/bart-base-lora-3DPrompt" \
    --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": "Miguelpef/bart-base-lora-3DPrompt",
		"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 "Miguelpef/bart-base-lora-3DPrompt" \
        --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": "Miguelpef/bart-base-lora-3DPrompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Miguelpef/bart-base-lora-3DPrompt with Docker Model Runner:
```
docker model run hf.co/Miguelpef/bart-base-lora-3DPrompt
```

Miguelpef commited on Jan 19, 2025

Commit

3d5ff07

verified ·

1 Parent(s): 7003edd

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

adapter_config.json +17 -0
adapter_model.bin +3 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,17 @@

+{
+  "base_model_name_or_path": "facebook/bart-base",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "SEQ_2_SEQ_LM"
+}

adapter_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:07269b50c8ed5e8a7eac2626d5d35b93831dd8288fc775f3bb831224607ac363
+size 3565574