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 9, 2024

Commit

62b20e6

1 Parent(s): df4503a

Update README.md

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@@ -32,7 +32,6 @@ Modellen er trænet med ChatML format (samme som OpenAI's modeller), og skal bru
 ```
 ## Inferens
 ### Ollama
@@ -122,13 +121,7 @@ print("AI:", chat_response)
 | 0.5099        | 2.47  | 1360 | 0.7193          |
 | 0.5217        | 2.72  | 1496 | 0.7168          |
-### Framework versions
-- Transformers 4.37.0.dev0
-- Pytorch 2.0.1+cu118
-- Datasets 2.16.1
-- Tokenizers 0.15.0
 ## Brug for hjælp?

 ```
 ## Inferens
 ### Ollama
 | 0.5099        | 2.47  | 1360 | 0.7193          |
 | 0.5217        | 2.72  | 1496 | 0.7168          |
 ## Brug for hjælp?
+Har du spørgsmål eller brug for hjælp til LLM'er eller automatisering af tekstbaserede opgaver, så kontakt mig gerne.
+/Mads