Instructions to use cackerman/distilgpt2_aug_LORA_CAUSAL_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cackerman/distilgpt2_aug_LORA_CAUSAL_LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cackerman/distilgpt2_aug_LORA_CAUSAL_LM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cackerman/distilgpt2_aug_LORA_CAUSAL_LM") model = AutoModelForCausalLM.from_pretrained("cackerman/distilgpt2_aug_LORA_CAUSAL_LM") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cackerman/distilgpt2_aug_LORA_CAUSAL_LM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cackerman/distilgpt2_aug_LORA_CAUSAL_LM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cackerman/distilgpt2_aug_LORA_CAUSAL_LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cackerman/distilgpt2_aug_LORA_CAUSAL_LM
- SGLang
How to use cackerman/distilgpt2_aug_LORA_CAUSAL_LM 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 "cackerman/distilgpt2_aug_LORA_CAUSAL_LM" \ --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": "cackerman/distilgpt2_aug_LORA_CAUSAL_LM", "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 "cackerman/distilgpt2_aug_LORA_CAUSAL_LM" \ --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": "cackerman/distilgpt2_aug_LORA_CAUSAL_LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cackerman/distilgpt2_aug_LORA_CAUSAL_LM with Docker Model Runner:
docker model run hf.co/cackerman/distilgpt2_aug_LORA_CAUSAL_LM
Upload model
Browse files- adapter_model.bin +2 -2
adapter_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:be93de365acd61a7a3f7ff0860fd9b09b47e9f7e30b08b86b2b702d98b6a161f
|
| 3 |
+
size 594133
|