Instructions to use basavyr/bitnet_b1_58-3B_quant_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basavyr/bitnet_b1_58-3B_quant_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="basavyr/bitnet_b1_58-3B_quant_gguf",
	filename="model_f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use basavyr/bitnet_b1_58-3B_quant_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf basavyr/bitnet_b1_58-3B_quant_gguf:F16
# Run inference directly in the terminal:
llama-cli -hf basavyr/bitnet_b1_58-3B_quant_gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf basavyr/bitnet_b1_58-3B_quant_gguf:F16
# Run inference directly in the terminal:
llama-cli -hf basavyr/bitnet_b1_58-3B_quant_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 basavyr/bitnet_b1_58-3B_quant_gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf basavyr/bitnet_b1_58-3B_quant_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 basavyr/bitnet_b1_58-3B_quant_gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf basavyr/bitnet_b1_58-3B_quant_gguf:F16

Use Docker

docker model run hf.co/basavyr/bitnet_b1_58-3B_quant_gguf:F16

LM Studio
Jan
Ollama
How to use basavyr/bitnet_b1_58-3B_quant_gguf with Ollama:
```
ollama run hf.co/basavyr/bitnet_b1_58-3B_quant_gguf:F16
```

Unsloth Studio new

How to use basavyr/bitnet_b1_58-3B_quant_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 basavyr/bitnet_b1_58-3B_quant_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 basavyr/bitnet_b1_58-3B_quant_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for basavyr/bitnet_b1_58-3B_quant_gguf to start chatting

Docker Model Runner
How to use basavyr/bitnet_b1_58-3B_quant_gguf with Docker Model Runner:
```
docker model run hf.co/basavyr/bitnet_b1_58-3B_quant_gguf:F16
```

Lemonade

How to use basavyr/bitnet_b1_58-3B_quant_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull basavyr/bitnet_b1_58-3B_quant_gguf:F16

Run and chat with the model

lemonade run user.bitnet_b1_58-3B_quant_gguf-F16

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

This model is a straightforward copy of the original 3B parameter model, but only with the following models:

Thanks to Green-Sky for also providing similar work.

HF to GGUF converted model in f16 precision -> model_f16.gguf
- It was converted using llama.cpp with this specific commit.
- Command: python3 path_to_llama_cpp/convert_hf_to_gguf.py --outfile ./model_f16.gguf --outtype f16
quantized (GGUF version) in Q1_3 format
- Quantization is done via llama-quantize on that same commit.
quantized (GGUF version) in Q2_2 format
- Quantization is done via llama-quantize on that same commit.

Please keep in mind that if you want to test this model through llama-cli on Metal (e.g., MacBook Pro with M3 Pro, as I did) you would need to use the --n-gpu-layers 0 flag, otherwise the following error will occur:

/Users/basavyr/Repos/external/llama.cpp/llama-cli -m model_quant_Q2_2.gguf -p "hey there"
Log start
main: build = 3505 (45719a24)
main: built with Apple clang version 15.0.0 (clang-1500.3.9.4) for arm64-apple-darwin23.6.0
main: seed  = 1724230525
llama_model_loader: loaded meta data with 30 key-value pairs and 470 tensors from model_quant_Q2_2.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.

.........................................................................................
llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M3 Pro
ggml_metal_init: picking default device: Apple M3 Pro
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name:   Apple M3 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple9  (1009)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction support   = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory              = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 12884.92 MB
llama_kv_cache_init:      Metal KV buffer size =   650.00 MiB
llama_new_context_with_model: KV self size  =  650.00 MiB, K (f16):  325.00 MiB, V (f16):  325.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB
llama_new_context_with_model:      Metal compute buffer size =   157.00 MiB
llama_new_context_with_model:        CPU compute buffer size =    62.50 MiB
llama_new_context_with_model: graph nodes  = 1124
llama_new_context_with_model: graph splits = 3
ggml/src/ggml-metal.m:1612: MUL MAT-MAT not implemented
ggml/src/ggml-metal.m:1612: MUL MAT-MAT not implemented[1]    26436 abort      /Users/basavyr/Repos/external/llama.cpp/llama-cli -m model_quant_Q2_2.gguf -p

Downloads last month: 21

GGUF

Model size

3B params

Architecture

bitnet

Hardware compatibility

16-bit

View +2 variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support