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

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1 Parent(s): f1cd101

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@@ -3,23 +3,28 @@ license: apache-2.0
 base_model: mhenrichsen/danskgpt-tiny
 tags:
 - generated_from_trainer
-model-index:
-- name: tiny-chat
-  results: []
 ---
 # DanskGPT-chat-tiny
-This model is a fine-tuned version of [mhenrichsen/danskgpt-tiny](https://huggingface.co/mhenrichsen/danskgpt-tiny) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.7168
-## Model description
-More information needed
-## Intended uses & limitations
 More information needed

 base_model: mhenrichsen/danskgpt-tiny
 tags:
 - generated_from_trainer
 ---
 # DanskGPT-chat-tiny
+DanskGPT-chat-tiny er chat-varianten af [mhenrichsen/danskgpt-tiny](https://huggingface.co/mhenrichsen/danskgpt-tiny) trænet på private chat datasæt.
+## Model beskrivelse
+Modellen er beregnet til at være en lightweight version af DanskGPT, der kan køre på næsten alle enheder.
+## Prompt template
+Modellen er trænet med ChatML format (samme som OpenAI's modeller), og skal bruges på følgende måde:
+```
+<|im_start|>system\nDu er en hjælpsom assistent.<|im_end|>\n<|im_start|>user\nHvad er skak?<|im_end|>\n<|im_start|>asistant
+```
 More information needed