Instructions to use Locutusque/gpt2-conversational-or-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/gpt2-conversational-or-qa with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/gpt2-conversational-or-qa")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/gpt2-conversational-or-qa")
model = AutoModelForCausalLM.from_pretrained("Locutusque/gpt2-conversational-or-qa")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Locutusque/gpt2-conversational-or-qa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/gpt2-conversational-or-qa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/gpt2-conversational-or-qa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Locutusque/gpt2-conversational-or-qa

SGLang

How to use Locutusque/gpt2-conversational-or-qa 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 "Locutusque/gpt2-conversational-or-qa" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/gpt2-conversational-or-qa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Locutusque/gpt2-conversational-or-qa" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/gpt2-conversational-or-qa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Locutusque/gpt2-conversational-or-qa with Docker Model Runner:
```
docker model run hf.co/Locutusque/gpt2-conversational-or-qa
```

Add default chat template to tokenizer_config.json

by Xenova HF Staff - opened Feb 16, 2024

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

-2

Files changed (1) hide show

tokenizer_config.json +3 -2

tokenizer_config.json CHANGED Viewed

@@ -29,5 +29,6 @@
     "normalized": true,
     "rstrip": false,
     "single_word": false
-  }
-}

     "normalized": true,
     "rstrip": false,
     "single_word": false
+  },
+  "chat_template": "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}"
+}