Instructions to use mNLP-project/gpt2-dpo-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mNLP-project/gpt2-dpo-quantized with Transformers:

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

pipe = pipeline("text-generation", model="mNLP-project/gpt2-dpo-quantized")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mNLP-project/gpt2-dpo-quantized")
model = AutoModelForCausalLM.from_pretrained("mNLP-project/gpt2-dpo-quantized")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mNLP-project/gpt2-dpo-quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mNLP-project/gpt2-dpo-quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mNLP-project/gpt2-dpo-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mNLP-project/gpt2-dpo-quantized

SGLang

How to use mNLP-project/gpt2-dpo-quantized 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 "mNLP-project/gpt2-dpo-quantized" \
    --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": "mNLP-project/gpt2-dpo-quantized",
		"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 "mNLP-project/gpt2-dpo-quantized" \
        --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": "mNLP-project/gpt2-dpo-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mNLP-project/gpt2-dpo-quantized with Docker Model Runner:
```
docker model run hf.co/mNLP-project/gpt2-dpo-quantized
```

Luca-Engel commited on Jun 10, 2024

Commit

81773dc

verified ·

1 Parent(s): 199f1dc

AutoGPTQ model for gpt2-dpo: 4bits, gr128, desc_act=False

Browse files

Files changed (2) hide show

gptq_model-4bit-128g.safetensors +3 -0
tokenizer.json +1 -6

gptq_model-4bit-128g.safetensors ADDED Viewed

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+oid sha256:73aafa8f515bc575739865c90734844c662e73cd9a0a8076af686a03ff326bd1
+size 200619312

tokenizer.json CHANGED Viewed

@@ -1,11 +1,6 @@
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-    "max_length": 50,
-    "strategy": "LongestFirst",
-    "stride": 0
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   "padding": null,
   "added_tokens": [
     {

 {
   "version": "1.0",
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   "added_tokens": [
     {