Instructions to use mardakani/Bitnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mardakani/Bitnet with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf mardakani/Bitnet:F32 # Run inference directly in the terminal: llama cli -hf mardakani/Bitnet:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mardakani/Bitnet:F32 # Run inference directly in the terminal: llama cli -hf mardakani/Bitnet:F32
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf mardakani/Bitnet:F32 # Run inference directly in the terminal: ./build/bin/llama cli -hf mardakani/Bitnet:F32
Use Docker
docker model run hf.co/mardakani/Bitnet:F32
- Llama new
- LM Studio
- Jan
- Ollama
How to use mardakani/Bitnet with Ollama:
ollama run hf.co/mardakani/Bitnet:F32
- Unsloth Desktop
- Docker Model Runner
How to use mardakani/Bitnet with Docker Model Runner:
docker model run hf.co/mardakani/Bitnet:F32
- Lemonade
How to use mardakani/Bitnet with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mardakani/Bitnet:F32
Run and chat with the model
lemonade run user.Bitnet-F32
List all available models
lemonade list
- Atomic Chat
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Download README.md from mardakani/Bitnet: direct link, hf CLI and curl.
- Browser
- Download file 2.24 kB
-
https://huggingface.co/mardakani/Bitnet/resolve/main/README.md
- Command line
-
hf download hf://mardakani/Bitnet/README.md
-
curl -L -o README.md https://huggingface.co/mardakani/Bitnet/resolve/main/README.md
2.24 kB
| license: mit | |
| This is a reproduction of the <a href="https://arxiv.org/abs/2402.17764"> BitNet b1.58</a> paper. The models are trained with <a href="https://github.com/togethercomputer/RedPajama-Data">RedPajama dataset</a> for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following <a href="https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf">paper</a>. All models are open-source in the <a href="https://huggingface.co/1bitLLM">repo</a>. We will train larger models and/or more tokens when resource is available. | |
| ## Results | |
| PPL and zero-shot accuracy: | |
| | Models | PPL| ARCe| ARCc| HS | BQ | OQ | PQ | WGe | Avg | |
| |-------|-------|-------|-------|-------|-------|-------|-------|-------|-------| | |
| | FP16 700M (reported) | 12.33 | 54.7 | 23.0 | 37.0 | 60.0 | 20.2 | 68.9 | 54.8 | 45.5 | | |
| | BitNet b1.58 700M (reported) | 12.87 | 51.8 | 21.4 | 35.1 | 58.2 | 20.0 | 68.1 | 55.2 | 44.3 | | |
| | BitNet b1.58 700M (reproduced) | 12.78 | 51.4 | 21.8 | 35.0 | 59.6 | 20.6 | 67.5 | 55.4 | 44.5 | | |
| | FP16 1.3B (reported) | 11.25 | 56.9 | 23.5 | 38.5 | 59.1 | 21.6 | 70.0 | 53.9 | 46.2 | |
| | BitNet b1.58 1.3B (reported) | 11.29 | 54.9 | 24.2 | 37.7 | 56.7 | 19.6 | 68.8 | 55.8 | 45.4 | | |
| | BitNet b1.58 1.3B (reproduced) | 11.19 | 55.8 | 23.7 | 37.6 | 59.0 | 20.2 | 69.2 | 56.0 | 45.9 | |
| | FP16 3B (reported) | 10.04 | 62.1 | 25.6 | 43.3 | 61.8 | 24.6 | 72.1 | 58.2 | 49.7 | |
| | BitNet b1.58 3B (reported) | 9.91 | 61.4 | 28.3 | 42.9 | 61.5 | 26.6 | 71.5 | 59.3 | 50.2 | |
| | BitNet b1.58 3B (reproduced) | 9.88 | 60.9 | 28.0 | 42.3 | 58.3 | 26.0 | 71.4 | 60.3 | 49.6 | | |
| The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors. | |
| ## Evaluation | |
| The evaluation pipelines are from the paper authors. Here is the commands to run the evaluation: | |
| ``` | |
| pip install lm-eval==0.3.0 | |
| ``` | |
| ``` | |
| python eval_ppl.py --hf_path 1bitLLM/bitnet_b1_58-3B --seqlen 2048 | |
| ``` | |
| ``` | |
| python eval_task.py --hf_path 1bitLLM/bitnet_b1_58-3B \ | |
| --batch_size 1 \ | |
| --tasks \ | |
| --output_path result.json \ | |
| --num_fewshot 0 \ | |
| --ctx_size 2048 | |
| ``` | |