Instructions to use TareksTesting/Scripturient-V1.0-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksTesting/Scripturient-V1.0-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="TareksTesting/Scripturient-V1.0-LLaMa-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TareksTesting/Scripturient-V1.0-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("TareksTesting/Scripturient-V1.0-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TareksTesting/Scripturient-V1.0-LLaMa-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TareksTesting/Scripturient-V1.0-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": "TareksTesting/Scripturient-V1.0-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TareksTesting/Scripturient-V1.0-LLaMa-70B

SGLang

How to use TareksTesting/Scripturient-V1.0-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 "TareksTesting/Scripturient-V1.0-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": "TareksTesting/Scripturient-V1.0-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 "TareksTesting/Scripturient-V1.0-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": "TareksTesting/Scripturient-V1.0-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksTesting/Scripturient-V1.0-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/TareksTesting/Scripturient-V1.0-LLaMa-70B
```

Scripturient-V1.0-LLaMa-70B / README.md

Tarek07

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	---
	base_model:
	- TareksLab/Ruby-D-V3-LLaMa-70B
	- TareksLab/Amethyst-SCE-V4-LLaMa-70B
	- TareksLab/Diamond-DL-V1-LLaMa-70B
	- TareksLab/Carnelian-SCE-V4-LLaMa-70B
	- TareksLab/Emerald-SCE-V3-LLaMa-70B
	- TareksLab/Citrine-MS-V3-LLaMa-70B
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# MERGE2

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method using [TareksLab/Diamond-DL-V1-LLaMa-70B](https://huggingface.co/TareksLab/Diamond-DL-V1-LLaMa-70B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [TareksLab/Ruby-D-V3-LLaMa-70B](https://huggingface.co/TareksLab/Ruby-D-V3-LLaMa-70B)
	* [TareksLab/Amethyst-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Amethyst-SCE-V4-LLaMa-70B)
	* [TareksLab/Carnelian-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Carnelian-SCE-V4-LLaMa-70B)
	* [TareksLab/Emerald-SCE-V3-LLaMa-70B](https://huggingface.co/TareksLab/Emerald-SCE-V3-LLaMa-70B)
	* [TareksLab/Citrine-MS-V3-LLaMa-70B](https://huggingface.co/TareksLab/Citrine-MS-V3-LLaMa-70B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: TareksLab/Emerald-SCE-V3-LLaMa-70B
	parameters:
	weight: [0.1, 0.1, 0.1, 0.2, 0.5]
	density: 0.5
	epsilon: 0.15
	- model: TareksLab/Carnelian-SCE-V4-LLaMa-70B
	parameters:
	weight: [0.1, 0.1, 0.2, 0.4, 0.2]
	density: 0.5
	epsilon: 0.15
	- model: TareksLab/Ruby-D-V3-LLaMa-70B
	parameters:
	weight: [0.1, 0.2, 0.4, 0.2, 0.1]
	density: 0.5
	epsilon: 0.15
	- model: TareksLab/Amethyst-SCE-V4-LLaMa-70B
	parameters:
	weight: [0.2, 0.4, 0.2, 0.1, 0.1]
	density: 0.5
	epsilon: 0.15
	- model: TareksLab/Citrine-MS-V3-LLaMa-70B
	parameters:
	weight: [0.5, 0.2, 0.1, 0.1, 0.1]
	density: 0.5
	epsilon: 0.15
	- model: TareksLab/Diamond-DL-V1-LLaMa-70B
	parameters:
	weight: 0.10
	density: 0.5
	epsilon: 0.15
	merge_method: della
	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

	```