Instructions to use kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha with Transformers:

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

pipe = pipeline("text-generation", model="kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha")
model = AutoModelForMultimodalLM.from_pretrained("kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha")
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 kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha

SGLang

How to use kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha 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 "kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha" \
    --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": "kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha",
		"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 "kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha" \
        --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": "kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha with Docker Model Runner:
```
docker model run hf.co/kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Alpesteibock-Llama-3-8B-Alpha

Alpesteibock-Llama-3-8B-Alpha is an experimental QLoRA fine-tune of NousResearch/Hermes-2-Pro-Llama-3-8B on a dataset of 34.7 million tokens of Swiss German text from multiple sources for two epochs.

License

This model is released under the Llama 3 Community License.

Usage

The model uses ChatML as an instruction template and was trained using "You are Alpesteibock, a helpful assistant who speaks Swiss German." as a system message:

<|im_start|>system
You are Alpesteibock, a helpful assistant who speaks Swiss German.<|im_end|>
<|im_start|>user
Hoi. Wie heissisch du?<|im_end|>
<|im_start|>assistant
Ich bi de Alpesteibock und ich freu mi uf di.<|im_end|>

Dataset

The dataset used for training consists of the following sources:

Dataset	File Size	Description	Phase
Glot500 Corpus (gsw_Latn, Leipzig_web)	21.7 MB	Text, usually sentences, crawled from the web	1
Alemannic Wikipedia (Subset)	50.5 MB	Articles in the Alemannic Wikipedia with most of those written in Alsatian filtered out	2
Schweizerdeutscher Mundartkorpus (Copyright Free Subset)	28.4 MB	Copyright free books written in Swiss German	2
GlotCC-V1.0 (gsw-Latn)	7.5 MB	Document-level general domain monolingual dataset derived from CommonCrawl	2
Synthetic Instruction Data	1.7 MB	Different datasets of synthetically generated Swiss German text	2

Training Details

Hardware: 1x RTX 4090
Duration: 40 hours in total (2 hours for first phase and 38 hours for second phase)

Hyperparameters

Adapter: QLoRA
Precision: 4-bit
Optimizer: adamw_bnb_8bit
LoRA Rank: 256
LoRA Alpha: 256
Learning Rate: 1e-5
Scheduler: Cosine
Context Length: 4096
Batch Size: 1
Gradient Accumulation Steps: 1
Sample Packing: On for first phase, Off for second phase
Epochs: 2

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for kaizuberbuehler/Alpesteibock-Llama-3-8B-Alpha

Base model

NousResearch/Meta-Llama-3-8B

Finetuned

NousResearch/Hermes-2-Pro-Llama-3-8B

Finetuned

(64)

this model

Quantizations

1 model

kaizuberbuehler
/

Alpesteibock-Llama-3-8B-Alpha