Instructions to use ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit with Transformers:

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

pipe = pipeline("text-generation", model="ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit")
model = AutoModelForCausalLM.from_pretrained("ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit", 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 ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit

SGLang

How to use ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit 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 "ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit" \
    --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": "ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit",
		"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 "ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit" \
        --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": "ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit with Docker Model Runner:
```
docker model run hf.co/ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit
```

Munin-7b-alpha instruction fined tuned

Munin-7b-alpha from Danish Foundation Models fine-tuned by yours truly for 1 epoch on kobprof/skolegpt-instruct using the code from this notebook by The Alexandra Institute

Trained on a single Nvidia RTX A4000 GPU using 13.82 GB GPU memory (87.84%), of which 8.71 GB (55.39%) was used for LoRa.

The model trained for just shy of 4 hours consuming a total of 0.694 KWh (as per estimates produced with CodeCarbon) and emitting approximately 57 gCO2e (average CO2e emissions per KWh during training was 82.5 g as per https://www.energidataservice.dk/tso-electricity/CO2Emis)

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit

Merges

1 model

ThatsGroes
/

munin-SkoleGPTOpenOrca-7b-16bit

Munin-7b-alpha instruction fined tuned

Model tree for ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit

Dataset used to train ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit