Instructions to use zerolimites/BitNet-TinyDialogues-1.58b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zerolimites/BitNet-TinyDialogues-1.58b with Transformers:

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

pipe = pipeline("text-generation", model="zerolimites/BitNet-TinyDialogues-1.58b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zerolimites/BitNet-TinyDialogues-1.58b")
model = AutoModelForCausalLM.from_pretrained("zerolimites/BitNet-TinyDialogues-1.58b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zerolimites/BitNet-TinyDialogues-1.58b 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 zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0
# Run inference directly in the terminal:
llama cli -hf zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0
# Run inference directly in the terminal:
llama cli -hf zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0

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 zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0

Use Docker

docker model run hf.co/zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0

Llama new
LM Studio
Jan

vLLM

How to use zerolimites/BitNet-TinyDialogues-1.58b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zerolimites/BitNet-TinyDialogues-1.58b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerolimites/BitNet-TinyDialogues-1.58b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0

SGLang

How to use zerolimites/BitNet-TinyDialogues-1.58b 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 "zerolimites/BitNet-TinyDialogues-1.58b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerolimites/BitNet-TinyDialogues-1.58b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "zerolimites/BitNet-TinyDialogues-1.58b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerolimites/BitNet-TinyDialogues-1.58b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use zerolimites/BitNet-TinyDialogues-1.58b with Ollama:
```
ollama run hf.co/zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0
```
Unsloth Desktop
Docker Model Runner
How to use zerolimites/BitNet-TinyDialogues-1.58b with Docker Model Runner:
```
docker model run hf.co/zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0
```

Lemonade

How to use zerolimites/BitNet-TinyDialogues-1.58b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zerolimites/BitNet-TinyDialogues-1.58b:TQ1_0

Run and chat with the model

lemonade run user.BitNet-TinyDialogues-1.58b-TQ1_0

List all available models

lemonade list

Atomic Chat

BitNet TinyDialogues 1.58b

Modelo experimental BitNet b1.58-style treinado do zero em CPU com styfeng/TinyDialogues.

O que é

Transformer causal pequeno com BitLinear: pesos ternários {-1, 0, +1} no forward (1,58 bit/peso).
Ativações quantizadas em 8 bits por token durante o treino (W1.58A8).
FFN com ReLU², RoPE, RMSNorm/subLN, embeddings tied.
GGUF: TQ1_0 e TQ2_0 (ternários). O arquivo também contém embeddings/normas em maior precisão.

“1-bit BitNet” aqui significa a família BitNet b1.58 ternária, não 1 bit matemático por peso.

Arquitetura

Campo	Valor
Parâmetros	~2,89 M
Camadas	4
Hidden	256
FFN	512
Heads	4 (KV 2)
Vocab	2048 BPE SentencePiece
Contexto	128 tokens
Treino	8000 steps, batch 8, seq 128, CPU
Melhor val loss	2.5838 (step 7000)
Perplexidade val	~13.25

Arquivos

gguf/bitnet-tinydialogues-TQ1_0.gguf — principal, menor (~1,1 MiB).
gguf/bitnet-tinydialogues-TQ2_0.gguf — alternativa ternária (~1,2 MiB).
gguf/bitnet-tinydialogues-f32.gguf — referência F32 (~11 MiB).
model.safetensors, config.json, tokenizer.model, tokenizer.json — checkpoint HF.
scripts/ — preparação, treino, exportação e demo.
patches/llama.cpp-bitnet-fixes.patch — patch ReLU² + mapeamento de tensores.
Dockerfile — Space Docker/Gradio para CPU (requer PRO ou hardware pago).

Uso rápido com llama.cpp

O runtime precisa usar ReLU² no FFN do BitNet. O llama.cpp upstream ainda pode usar SiLU hardcoded; aplique patches/llama.cpp-bitnet-fixes.patch ou use o Dockerfile.

llama-cli -m gguf/bitnet-tinydialogues-TQ1_0.gguf \
  -p "user: can we play? assistant:" -n 64 --temp 0.7 -t 2 -r "user:"

Exemplo real (TQ1_0, greedy):

user: can we play? assistant: yes, but we need to clean up. we need to clean up.

HF demo (ternário)

python scripts/demo_hf.py --model . --prompt "user: can we play? assistant:" --tokens 40

Limitações

Modelo muito pequeno, treinado apenas em diálogos infantis em inglês. Serve para demonstrar BitNet/GGUF/CPU; não é um assistente geral nem tem conhecimento confiável.

Licença

MIT. Dataset TinyDialogues: MIT.

Downloads last month: 46

Safetensors

Model size

2.89M params

Tensor type

F32

zerolimites
/

BitNet-TinyDialogues-1.58b