Instructions to use Raziel1234/Duchifat-1-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Raziel1234/Duchifat-1-Base with Transformers:

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

pipe = pipeline("text-generation", model="Raziel1234/Duchifat-1-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Raziel1234/Duchifat-1-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Raziel1234/Duchifat-1-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Raziel1234/Duchifat-1-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raziel1234/Duchifat-1-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Raziel1234/Duchifat-1-Base

SGLang

How to use Raziel1234/Duchifat-1-Base 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 "Raziel1234/Duchifat-1-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raziel1234/Duchifat-1-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Raziel1234/Duchifat-1-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raziel1234/Duchifat-1-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Raziel1234/Duchifat-1-Base with Docker Model Runner:
```
docker model run hf.co/Raziel1234/Duchifat-1-Base
```

🦜 Duchifat-1-Base (דוכיפת)

1. Overview

Duchifat-1-Base is a state-of-the-art, small-scale Hebrew Language Model (LLM) built on a custom Llama-based architecture. With 132 Million parameters, it is designed for efficiency, speed, and deep understanding of Hebrew syntax and urban semantics.

The model was trained from scratch to demonstrate that high-quality Hebrew text generation is possible even with limited parameter counts, provided the architecture is optimized for the language's unique morphological structure.

2. Model Specifications

Architecture: Llama-2-style (Decoder-only Transformer)
Parameters: 132 Million
Vocabulary Size: 52,000 (AlephBERT Tokenizer)
Context Length: 1024 tokens
Training Device: Dual NVIDIA T4 GPUs
License: Apache 2.0

3. Key Capabilities

Syntactic Precision: Exceptional grasp of Hebrew prefixes (ו-כש-ל-ב) and sentence structure.
Domain Knowledge: Strong performance in social media context, financial reporting terminology, and sociological discourse.
Efficiency: Capable of running on edge devices and mobile hardware with minimal latency.

4. Training Data

The model was pre-trained on the Hebrew Space Restoration Corpus by Dicta-IL, focusing on:

Social media interactions and contemporary slang.
Formal news reporting and financial data.
Modern Hebrew literature and essays.

5. Usage (Python)

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_id = "Raziel1234/Duchifat-1-Base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

# Inference
prompt = "מצד שני ניכרים היום סימנים של"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False).to("cuda")

output = model.generate(**inputs, max_new_tokens=50, temperature=0.6, do_sample=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Downloads last month: 12

Safetensors

Model size

0.2B params

Tensor type

F32

Raziel1234
/

Duchifat-1-Base