Instructions to use Tarek07/Scripturient-V1.3-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tarek07/Scripturient-V1.3-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="Tarek07/Scripturient-V1.3-LLaMa-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tarek07/Scripturient-V1.3-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("Tarek07/Scripturient-V1.3-LLaMa-70B")
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

vLLM

How to use Tarek07/Scripturient-V1.3-LLaMa-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tarek07/Scripturient-V1.3-LLaMa-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tarek07/Scripturient-V1.3-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tarek07/Scripturient-V1.3-LLaMa-70B

SGLang

How to use Tarek07/Scripturient-V1.3-LLaMa-70B 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 "Tarek07/Scripturient-V1.3-LLaMa-70B" \
    --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": "Tarek07/Scripturient-V1.3-LLaMa-70B",
		"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 "Tarek07/Scripturient-V1.3-LLaMa-70B" \
        --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": "Tarek07/Scripturient-V1.3-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tarek07/Scripturient-V1.3-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/Tarek07/Scripturient-V1.3-LLaMa-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Scripturient is a culmination of my ongoing experiments with merging specialized curated models. Designed to keep creativity high, without sacrificing stability.

As for samplers, the model doesn't need samplers to reign it in much at all. My recommendation is:

Temp: 1
Min P: 0.01

That being said, it can handle even higher temperatures and Nsigma works well too.

Because of the nature of this sort of 'Hyper Multi Model Merge', my recommendation is not to run this on anything lower than a Q5 quant.

If you enjoy my work, please consider supporting me, It helps me make more models like this! Support on KO-FI <3

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: TareksLab/Diamond-DL-V1-LLaMa-70B
    parameters:
      weight: 0.10
      density: 0.7
      epsilon: 0.20
  - model: TareksLab/Citrine-MS-V3-LLaMa-70B
    parameters:
      weight: [0.5, 0.2, 0.1, 0.1, 0.1]
      density: 0.7
      epsilon: 0.20
  - model: TareksLab/Amethyst-SCE-V4-LLaMa-70B
    parameters:
      weight: [0.2, 0.4, 0.2, 0.1, 0.1]
      density: 0.7
      epsilon: 0.20
  - model: TareksLab/Ruby-D-V3-LLaMa-70B
    parameters:
      weight: [0.1, 0.2, 0.4, 0.2, 0.1]
      density: 0.7
      epsilon: 0.20
  - model: TareksLab/Carnelian-SCE-V4-LLaMa-70B
    parameters:
      weight: [0.1, 0.1, 0.2, 0.4, 0.2]
      density: 0.7
      epsilon: 0.20
  - model: TareksLab/Emerald-SCE-V3-LLaMa-70B
    parameters:
      weight: [0.1, 0.1, 0.1, 0.2, 0.5]
      density: 0.7
      epsilon: 0.20
merge_method: della_linear
base_model: TareksLab/Diamond-DL-V1-LLaMa-70B
parameters:
  lambda: 1.1
  normalize: false
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
 source: TareksLab/Ruby-D-V3-LLaMa-70B
 pad_to_multiple_of: 8