Instructions to use Aleteian/PersonalitySaiga with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aleteian/PersonalitySaiga with Transformers:

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

pipe = pipeline("text-generation", model="Aleteian/PersonalitySaiga")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aleteian/PersonalitySaiga")
model = AutoModelForCausalLM.from_pretrained("Aleteian/PersonalitySaiga")
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 Aleteian/PersonalitySaiga with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Aleteian/PersonalitySaiga

SGLang

How to use Aleteian/PersonalitySaiga 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 "Aleteian/PersonalitySaiga" \
    --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": "Aleteian/PersonalitySaiga",
		"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 "Aleteian/PersonalitySaiga" \
        --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": "Aleteian/PersonalitySaiga",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aleteian/PersonalitySaiga with Docker Model Runner:
```
docker model run hf.co/Aleteian/PersonalitySaiga
```
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 TIES merge method using win10/Mistral-Nemo-abliterated-Nemo-Pro-v2 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: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
    parameters:
      density: 1
      weight: 1

  - model: Sao10K/MN-BackyardAI-Party-12B-v1
    parameters:
      density: 1
      weight: 1

  - model: IlyaGusev/saiga_nemo_12b
    parameters:
      density: 1
      weight: 1    

merge_method: ties
base_model: win10/Mistral-Nemo-abliterated-Nemo-Pro-v2
  
dtype: float32
chat_template: chatml
# 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