Instructions to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/Hermes-3-Llama-3.1-8B-lorablated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/Hermes-3-Llama-3.1-8B-lorablated")
model = AutoModelForCausalLM.from_pretrained("mlabonne/Hermes-3-Llama-3.1-8B-lorablated", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/Hermes-3-Llama-3.1-8B-lorablated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/Hermes-3-Llama-3.1-8B-lorablated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated

SGLang

How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated 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 "mlabonne/Hermes-3-Llama-3.1-8B-lorablated" \
    --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": "mlabonne/Hermes-3-Llama-3.1-8B-lorablated",
		"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 "mlabonne/Hermes-3-Llama-3.1-8B-lorablated" \
        --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": "mlabonne/Hermes-3-Llama-3.1-8B-lorablated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated with Docker Model Runner:
```
docker model run hf.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🪽 Hermes-3-Llama-3.1-8B-lorablated

70B version: mlabonne/Hermes-3-Llama-3.1-70B-lorablated

This is an uncensored version of NousResearch/Hermes-3-Llama-3.1-8B using lorablation.

You can see in the following example how Hermes 3 refuses to answer a legitimate question while the abliterated model complies:

The recipe is based on @grimjim's grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter (special thanks):

Extraction: We extract a LoRA adapter by comparing two models: a censored Llama 3.1 (meta-llama/Meta-Llama-3.1-8B-Instruct) and an abliterated Llama 3.1 (mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated).
Merge: We merge this new LoRA adapter using task arithmetic to the censored NousResearch/Hermes-3-Llama-3.1-8B to abliterate it.

See this article to learn more about abliteration.

⚡ Quantization

GGUF: https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF

🧩 Configuration

This model was merged using the task arithmetic merge method using NousResearch/Hermes-3-Llama-3.1-8B + Llama-3.1-8B-Instruct-abliterated-LORA as a base.

The following YAML configuration was used to produce this model:

base_model: NousResearch/Hermes-3-Llama-3.1-8B+Llama-3.1-8B-Instruct-abliterated-LORA
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: NousResearch/Hermes-3-Llama-3.1-8B+Llama-3.1-8B-Instruct-abliterated-LORA
    parameters:
      weight: 1.0

You can reproduce this model using the following commands:

# Setup
git clone https://github.com/arcee-ai/mergekit.git
cd mergekit && pip install -e .
pip install bitsandbytes

# Extraction
mergekit-extract-lora mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated meta-llama/Meta-Llama-3.1-8B-Instruct Llama-3.1-8B-Instruct-abliterated-LORA --rank=64

# Merge using previous config
mergekit-yaml config.yaml Hermes-3-Llama-3.1-8B-lorablated --allow-crimes --lora-merge-cache=./cache