inferbit 0.2.3

Run any open LLM on CPU. One command.

InferBit

v0.2.0 — Run any open LLM on CPU. One command.

pip install inferbit[cli]
inferbit quantize mistralai/Mistral-7B-Instruct-v0.3 -o model.ibf
inferbit chat model.ibf

InferBit converts HuggingFace models to optimized INT4 and runs them on any CPU (Apple Silicon, x86) with no GPU, no Docker, and no complex setup.

Install

# Library only
pip install inferbit

# Library + CLI
pip install inferbit[cli]

# Everything (library + CLI + server)
pip install inferbit[all]

Requires Python 3.9+. Works on macOS (ARM/Intel) and Linux (x86_64).

Quickstart

Command line

# Convert any HuggingFace model to INT4
inferbit quantize meta-llama/Llama-3.2-1B -o llama.ibf

# Convert a local safetensors file
inferbit quantize ./model.safetensors -o model.ibf

# Convert from Ollama (if installed)
inferbit quantize ollama://llama3:8b -o llama3.ibf

# Interactive chat
inferbit chat model.ibf

# Benchmark
inferbit bench model.ibf --tokens 128 --runs 3

# Model info
inferbit info model.ibf

# Serve with OpenAI-compatible API
inferbit serve model.ibf --port 8000

Python API

from inferbit import InferbitModel

# Load from HuggingFace (downloads, converts, and loads automatically)
model = InferbitModel.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.3",
    bits=4,
)

# Generate text
output = model.generate("Explain gravity in one sentence:")
print(output)
# "Gravity is the force that attracts objects with mass towards each other."

# Stream tokens
for token in model.stream("Write a haiku about mountains:"):
    print(token, end="", flush=True)

# Or load a pre-converted model
model = InferbitModel.load("model.ibf")

Convert separately

from inferbit import convert

# Convert safetensors to IBF
convert("model.safetensors", "model.ibf", bits=4, sensitive_bits=8)

# Convert a HuggingFace directory (with config.json + sharded safetensors)
convert("./model_dir/", "model.ibf", bits=4)

# Convert with progress callback
convert("model.safetensors", "model.ibf", progress=lambda pct, stage: print(f"{pct:.0%} {stage}"))

Token-level API

from inferbit import InferbitModel

model = InferbitModel.load("model.ibf")

# Work with raw token IDs
token_ids = model.generate_tokens([1, 2, 3, 4, 5], max_tokens=20, temperature=0.7)

# Get raw logits
logits = model.forward([1, 2, 3])

# KV cache control
model.kv_clear()
model.kv_truncate(512)
print(model.kv_length)

Model info

model = InferbitModel.load("model.ibf")
print(model.architecture)   # "llama"
print(model.num_layers)      # 32
print(model.hidden_size)     # 4096
print(model.vocab_size)      # 32768
print(model.max_context)     # 32768
print(model.bits)            # 4
print(model.total_memory_mb) # 3971.0

Quality-gated quantization

from inferbit import search_quantization_profile, EvalGates

# Automatically find the most aggressive quantization that meets quality targets
result = search_quantization_profile(
    "model.safetensors",
    output_dir="./models",
    gates=EvalGates(max_perplexity=10.0, min_tokens_per_sec=5.0),
)
print(f"Selected: {result.selected.name} ({result.selected.bits}-bit)")
print(f"Speed: {result.eval_result.tokens_per_sec:.1f} tok/s")

Supported Sources

Source	Example
HuggingFace Hub	inferbit quantize mistralai/Mistral-7B-Instruct-v0.3
Local safetensors	inferbit quantize model.safetensors
Sharded safetensors directory	inferbit quantize ./model_dir/
Local GGUF	inferbit quantize model.gguf
Ollama models	inferbit quantize ollama://llama3:8b

Supported Models

Any LLaMA-family architecture with public weights:

• LLaMA 2, LLaMA 3, LLaMA 3.2
• Mistral, Mixtral
• TinyLlama
• Code Llama
• And any model with the same architecture (GQA/MQA/MHA, RMSNorm, SiLU, RoPE)

Benchmarks

Apple Silicon, INT4 + INT8 attention, 8 threads:

Model	File size	Decode speed	Quality
TinyLlama 1.1B	643 MB	34.6 tok/s	Good
Mistral 7B	3,971 MB	6.8 tok/s	Excellent

Compression: 3.5x vs FP16 source. No retraining required.

How it works

1. Convert: reads safetensors/GGUF weights, quantizes to INT4 (MLP layers) and INT8 (attention/embeddings), packs into an optimized .ibf binary format
2. Load: memory-maps the .ibf file for instant loading
3. Run: SIMD-optimized kernels (NEON on ARM, AVX2 on x86) with multi-threaded matmul and parallel attention heads

The .ibf format is designed for fast loading: 64-byte aligned, mmap-friendly, no parsing at load time.

Configuration

Quantization

Flag	Default	Description
--bits	4	Weight quantization (2, 4, 8)
--sensitive-bits	8	Attention/embedding bits
--sparsity	0.0	Structured sparsity (0.0-0.6)

Generation

Flag	Default	Description
--temperature	0.7	Sampling temperature
--top-k	40	Top-K sampling
--top-p	0.9	Nucleus sampling
--max-tokens	512	Max tokens to generate
--threads	auto	CPU threads

Architecture

libinferbit (C shared library)
    |
    +-- Python: pip install inferbit
    +-- Node.js: npm install @inferbit/node (coming soon)

Single C engine, multiple language bindings. Same model, same results, any language.

License

MIT