Instructions to use bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407 with Transformers:

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

pipe = pipeline("text-generation", model="bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407")
model = AutoModelForCausalLM.from_pretrained("bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407", 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 bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407

SGLang

How to use bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407 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 "bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407" \
    --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": "bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407",
		"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 "bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407" \
        --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": "bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407 with Docker Model Runner:
```
docker model run hf.co/bamec66557/Mistral-Nemo-VICIOUS_MESH-12B-2407
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: bamec66557/VICIOUS_MESH-12B-OMEGA
dtype: bfloat16
merge_method: slerp
tokenizer_source: base

# Slices Configuration
slices:
  - sources:
      - model: bamec66557/VICIOUS_MESH-12B-OMEGA
        layer_range: [0, 10]
      - model: bamec66557/VICIOUS_MESH-12B-BETA
        layer_range: [0, 10]
    parameters:
      t:
        - name: self_attn
          value: [0.5, 0.55, 0.6, 0.65, 0.7]
        - name: mlp
          value: [1.0, 1.05, 1.1, 1.15, 1.2]
        - name: layer_norm
          value: [0.9, 0.95, 1.0, 1.05, 1.1]

  - sources:
      - model: bamec66557/VICIOUS_MESH-12B-OMEGA
        layer_range: [10, 20]
      - model: bamec66557/VICIOUS_MESH-12B-BETA
        layer_range: [10, 20]
    parameters:
      t:
        - name: self_attn
          value: [0.4, 0.45, 0.5, 0.55, 0.6]
        - name: mlp
          value: [1.1, 1.15, 1.2, 1.25, 1.3]
        - name: layer_norm
          value: [1.0, 1.05, 1.1, 1.15, 1.2]

  - sources:
      - model: bamec66557/VICIOUS_MESH-12B-OMEGA
        layer_range: [20, 30]
      - model: bamec66557/VICIOUS_MESH-12B-BETA
        layer_range: [20, 30]
    parameters:
      t:
        - name: self_attn
          value: [0.6, 0.65, 0.7, 0.75, 0.8]
        - name: mlp
          value: [0.9, 0.95, 1.0, 1.05, 1.1]
        - name: layer_norm
          value: [0.85, 0.9, 0.95, 1.0, 1.05]

  - sources:
      - model: bamec66557/VICIOUS_MESH-12B-OMEGA
        layer_range: [30, 40]
      - model: bamec66557/VICIOUS_MESH-12B-BETA
        layer_range: [30, 40]
    parameters:
      t:
        - name: self_attn
          value: [0.7, 0.75, 0.8, 0.85, 0.9]
        - name: mlp
          value: [0.8, 0.85, 0.9, 0.95, 1.0]
        - name: layer_norm
          value: [0.8, 0.85, 0.9, 0.95, 1.0]

# Regularization
regularization:
  - method: gradient_penalty
    scale: 0.05  # Increased influence for gradient control
  - method: weight_clipping
    clip_range: [-0.2, 0.2]  # Broader clipping range for flexibility
  - method: random_noise
    scale: 0.01  # Stronger noise injection
  - method: attention_dropout
    scale: 0.1  # Higher dropout to reduce attention fixation

# Postprocessing
postprocessing:
  - operation: entropy_regularization
    scale: 0.05  # Stronger encouragement for diverse outputs
  - operation: non_linear_scaling
    parameters:
      function: tanh
  - operation: sharpening
    intensity: 0.5  # Enhanced sharpening for precise outputs
  - operation: gaussian_smoothing
    sigma: 1.5  # Increased smoothing for stable outputs
  - operation: normalize
  - operation: dynamic_scaling
    scale_range: [0.8, 1.2]  # Expanded dynamic range for scaling
  - operation: smoothing
    parameters:
      adaptive: true
      range: [0.85, 1.15]  # Wider adaptive smoothing range
      kernel_size: 5