Instructions to use Edens-Gate/madness-nemo-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edens-Gate/madness-nemo-12b with Transformers:

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

pipe = pipeline("text-generation", model="Edens-Gate/madness-nemo-12b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/madness-nemo-12b")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/madness-nemo-12b", 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 Edens-Gate/madness-nemo-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edens-Gate/madness-nemo-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edens-Gate/madness-nemo-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edens-Gate/madness-nemo-12b

SGLang

How to use Edens-Gate/madness-nemo-12b 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 "Edens-Gate/madness-nemo-12b" \
    --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": "Edens-Gate/madness-nemo-12b",
		"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 "Edens-Gate/madness-nemo-12b" \
        --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": "Edens-Gate/madness-nemo-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edens-Gate/madness-nemo-12b with Docker Model Runner:
```
docker model run hf.co/Edens-Gate/madness-nemo-12b
```

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using NewEden/nemo-erebus 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: grimjim/mistralai-Mistral-Nemo-Instruct-2407
    parameters: 
      density: 0.2
      weight: 0.23
  - model: nbeerbower/mistral-nemo-bophades-12B
    parameters: 
      density: 0.2
      weight: 0.43
  - model: nbeerbower/mistral-nemo-gutenberg-12B-v4
    parameters:
      density: 0.2
      weight: 0.43
  - model: TheDrummer/UnslopNemo-12B-v4.1
    parameters:
      density: 0.5
      weight: 0.63

merge_method: task_arithmetic
base_model: NewEden/nemo-erebus
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

layer_parameters:
  - range: [0, 10]
    density_multiplier: 1.2
  - range: [10, 20]
    density_multiplier: 1.0
  - range: [20, 30]
    density_multiplier: 0.8
  - range: [30, 40]
    density_multiplier: 0.6

regularization:
  - method: gradient_penalty
    scale: 0.05
  - method: weight_clipping
    clip_range: [-0.2, 0.2]
  - method: random_noise
    scale: 0.01
  - method: attention_dropout
    scale: 0.1

postprocessing:
  - operation: entropy_regularization
    scale: 0.05
  - operation: non_linear_scaling
    parameters:
      function: tanh
  - operation: sharpening
    intensity: 0.5
  - operation: gaussian_smoothing
    sigma: 1.5
  - operation: normalize
  - operation: dynamic_scaling
    scale_range: [0.8, 1.2]
  - operation: smoothing
    parameters:
      adaptive: true
      range: [0.85, 1.15]
      kernel_size: 5