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 / scripts /assemble.py

zerolimites

BitNet b1.58 TinyDialogues: trained checkpoint, GGUF, scripts, patches

655f931 verified 2 days ago

Raw History Blame Contribute Delete

754 Bytes

#!/usr/bin/env python3
import json, shutil
from pathlib import Path

ROOT = Path('/home/user/bitnet-tinydialogues')
TRAIN = Path('/tmp/bitnet-work/train')
DATA = Path('/tmp/bitnet-work/data')
best = TRAIN / 'best_model'

for f in best.iterdir():
    if f.name in {'README.md'}:
        continue
    shutil.copy2(f, ROOT / f.name)
shutil.copy2(DATA / 'tokenizer.json', ROOT / 'tokenizer.json')
(ROOT / 'training').mkdir(exist_ok=True)
for f in ['history.json', 'best.json']:
    if (TRAIN / f).exists():
        shutil.copy2(TRAIN / f, ROOT / 'training' / f)
if (DATA / 'stats.json').exists():
    shutil.copy2(DATA / 'stats.json', ROOT / 'training' / 'data_stats.json')
shutil.copy2(ROOT / 'space' / 'Dockerfile', ROOT / 'Dockerfile')
print('assembled')

	#!/usr/bin/env python3
	import json, shutil
	from pathlib import Path

	ROOT = Path('/home/user/bitnet-tinydialogues')
	TRAIN = Path('/tmp/bitnet-work/train')
	DATA = Path('/tmp/bitnet-work/data')
	best = TRAIN / 'best_model'

	for f in best.iterdir():
	if f.name in {'README.md'}:
	continue
	shutil.copy2(f, ROOT / f.name)
	shutil.copy2(DATA / 'tokenizer.json', ROOT / 'tokenizer.json')
	(ROOT / 'training').mkdir(exist_ok=True)
	for f in ['history.json', 'best.json']:
	if (TRAIN / f).exists():
	shutil.copy2(TRAIN / f, ROOT / 'training' / f)
	if (DATA / 'stats.json').exists():
	shutil.copy2(DATA / 'stats.json', ROOT / 'training' / 'data_stats.json')
	shutil.copy2(ROOT / 'space' / 'Dockerfile', ROOT / 'Dockerfile')
	print('assembled')