Instructions to use DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental with Transformers:

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

pipe = pipeline("text-generation", model="DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental")
model = AutoModelForCausalLM.from_pretrained("DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental")
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

vLLM

How to use DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental

SGLang

How to use DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental 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 "DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental" \
    --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": "DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental",
		"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 "DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental" \
        --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": "DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental with Docker Model Runner:
```
docker model run hf.co/DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama3_DiscoLeo_8B_DARE_Experimental

DiscoResearch/Llama3_German_8B_v0.1 is a large language model based on Meta's Llama3-8B. It is specialized for the German language through continuous pretraining on 65 billion high-quality tokens, similar to previous LeoLM or Occiglot models.

This is a merge of our instruct model with the Instruct model by Meta. Created using mergekit. Contributed by Damian B.!

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using meta-llama/Meta-Llama-3-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: DiscoResearch/Llama3_DiscoLeo_Instruct_8B_v0.1
    parameters:
      density: 0.5
      weight: 0.5
  - model: meta-llama/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.5
      weight: 0.5
merge_method: dare_ties
base_model: meta-llama/Meta-Llama-3-8B
parameters:
  normalize: true
  int8_mask: false
dtype: bfloat16