Instructions to use mhenrichsen/danskgpt-tiny-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mhenrichsen/danskgpt-tiny-chat with Transformers:

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

pipe = pipeline("text-generation", model="mhenrichsen/danskgpt-tiny-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mhenrichsen/danskgpt-tiny-chat")
model = AutoModelForCausalLM.from_pretrained("mhenrichsen/danskgpt-tiny-chat", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mhenrichsen/danskgpt-tiny-chat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mhenrichsen/danskgpt-tiny-chat

SGLang

How to use mhenrichsen/danskgpt-tiny-chat 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 "mhenrichsen/danskgpt-tiny-chat" \
    --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": "mhenrichsen/danskgpt-tiny-chat",
		"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 "mhenrichsen/danskgpt-tiny-chat" \
        --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": "mhenrichsen/danskgpt-tiny-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mhenrichsen/danskgpt-tiny-chat with Docker Model Runner:
```
docker model run hf.co/mhenrichsen/danskgpt-tiny-chat
```

mhenrichsen commited on Jan 6, 2024

Commit

ccebf8d

1 Parent(s): 67d8cc7

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@@ -60,6 +60,27 @@ eller som endpoint:
 ```
 python -m vllm.entrypoints.openai.api_server --model mhenrichsen/danskgpt-tiny-chat
 ```
 ## Training procedure

 ```
 python -m vllm.entrypoints.openai.api_server --model mhenrichsen/danskgpt-tiny-chat
 ```
+og brugt som erstatning for OpenAI's endpoints:
+```python
+from openai import OpenAI
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+chat_response = client.chat.completions.create(
+    model="mhenrichsen/danskgpt-tiny-chat",
+    messages=[
+        {"role": "system", "content": "Du er en hjælpsom assistent. Giv mig et langt svar."},
+        {"role": "user", "content": "Fortæl mig om Danmark."},
+    ]
+)
+print("AI:", chat_response)
+```
 ## Training procedure