Instructions to use dicta-il/dictalm2.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use dicta-il/dictalm2.0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dicta-il/dictalm2.0-GGUF", filename="dictalm2.0.F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dicta-il/dictalm2.0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dicta-il/dictalm2.0-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf dicta-il/dictalm2.0-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dicta-il/dictalm2.0-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf dicta-il/dictalm2.0-GGUF:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf dicta-il/dictalm2.0-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf dicta-il/dictalm2.0-GGUF:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf dicta-il/dictalm2.0-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf dicta-il/dictalm2.0-GGUF:F16
Use Docker
docker model run hf.co/dicta-il/dictalm2.0-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use dicta-il/dictalm2.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dicta-il/dictalm2.0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dicta-il/dictalm2.0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dicta-il/dictalm2.0-GGUF:F16
- Ollama
How to use dicta-il/dictalm2.0-GGUF with Ollama:
ollama run hf.co/dicta-il/dictalm2.0-GGUF:F16
- Unsloth Studio
How to use dicta-il/dictalm2.0-GGUF 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 dicta-il/dictalm2.0-GGUF 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 dicta-il/dictalm2.0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dicta-il/dictalm2.0-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use dicta-il/dictalm2.0-GGUF with Docker Model Runner:
docker model run hf.co/dicta-il/dictalm2.0-GGUF:F16
- Lemonade
How to use dicta-il/dictalm2.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dicta-il/dictalm2.0-GGUF:F16
Run and chat with the model
lemonade run user.dictalm2.0-GGUF-F16
List all available models
lemonade list
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities
The DictaLM-2.0 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters trained to specialize in Hebrew text.
For full details of this model please read our release blog post or the technical report.
This is the base model designed for completion (not for chat!) in the GGUF format for use with llama.cpp.
There are two versions available - float16 precision (*.F16.gguf) and 4-bit quantized precision (*.Q4_K_M.gguf).
You can view and access the full collection of base/instruct unquantized/quantized versions of DictaLM-2.0 here.
Model Architecture
DictaLM-2.0 is based on the Mistral-7B-v0.1 model with the following changes:
- An extended tokenizer with 1,000 injected tokens specifically for Hebrew, increasing the compression rate from 5.78 tokens/word to 2.76 tokens/word.
- Continued pretraining on over 190B tokens of naturally occuring text, 50% Hebrew and 50% English.
Notice
DictaLM 2.0 is a pretrained base model and therefore does not have any moderation mechanisms.
Citation
If you use this model, please cite:
@misc{shmidman2024adaptingllmshebrewunveiling,
title={Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities},
author={Shaltiel Shmidman and Avi Shmidman and Amir DN Cohen and Moshe Koppel},
year={2024},
eprint={2407.07080},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.07080},
}
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dicta-il/dictalm2.0-GGUF", filename="", )