Instructions to use aixonlab/Zara-14b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aixonlab/Zara-14b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="aixonlab/Zara-14b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aixonlab/Zara-14b-v1")
model = AutoModelForCausalLM.from_pretrained("aixonlab/Zara-14b-v1")
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 aixonlab/Zara-14b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aixonlab/Zara-14b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aixonlab/Zara-14b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aixonlab/Zara-14b-v1

SGLang

How to use aixonlab/Zara-14b-v1 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 "aixonlab/Zara-14b-v1" \
    --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": "aixonlab/Zara-14b-v1",
		"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 "aixonlab/Zara-14b-v1" \
        --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": "aixonlab/Zara-14b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use aixonlab/Zara-14b-v1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aixonlab/Zara-14b-v1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aixonlab/Zara-14b-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aixonlab/Zara-14b-v1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="aixonlab/Zara-14b-v1",
    max_seq_length=2048,
)

Docker Model Runner
How to use aixonlab/Zara-14b-v1 with Docker Model Runner:
```
docker model run hf.co/aixonlab/Zara-14b-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Zara 14b v1 🧙‍♂️

Zara 14b tries to become the perfect companion for any chat which involves multiple roles. The ability to understand context is pretty awesome and excels in creativity and storytelling. It is built on Lamarck 14B v0.7 and trained on different datasets as well as some layer merges to ehance its capabilities.

Model Details 📊

Developed by: Aixon Lab
Model type: Causal Language Model
Language(s): English (primarily), may support other languages
License: Apache 2.0
Repository: https://huggingface.co/aixonlab/Zara-14b-v1

Quantization

GGUF: https://huggingface.co/mradermacher/Zara-14b-v1-GGUF
iMatrix GGUF: https://huggingface.co/mradermacher/Zara-14b-v1-i1-GGUF

Model Architecture 🏗️

Base model: sometimesanotion/Lamarck-14B-v0.7
Parameter count: ~14 billion
Architecture specifics: Transformer-based language model

Intended Use 🎯

As an advanced language model for various natural language processing tasks, including but not limited to text generation (excels in chat), question-answering, and analysis.

Ethical Considerations 🤔

As a model based on multiple sources, Zara 14b may inherit biases and limitations from its constituent models. Users should be aware of potential biases in generated content and use the model responsibly.

Performance and Evaluation

Performance metrics and evaluation results for Zara 14b are yet to be determined. Users are encouraged to contribute their findings and benchmarks.

Limitations and Biases

The model may exhibit biases present in its training data and constituent models. It's crucial to critically evaluate the model's outputs and use them in conjunction with human judgment.

Additional Information

For more details on the base model and constituent models, please refer to their respective model cards and documentation.

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Safetensors

Model size

15B params

Tensor type

BF16

Model tree for aixonlab/Zara-14b-v1

Base model

sometimesanotion/Lamarck-14B-v0.7

Finetuned

(2)

this model

Quantizations

2 models