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

File size: 1,257 Bytes

655f931

diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index ff22a60..783b148 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1355,10 +1355,12 @@ class TensorNameMap:
 
         MODEL_TENSOR.ATTN_SUB_NORM: (
             "model.layers.{bid}.self_attn.inner_attn_ln",  # bitnet
+            "model.layers.{bid}.self_attn.attn_sub_norm",  # bitnet (transformers >= 4.57)
         ),
 
         MODEL_TENSOR.FFN_SUB_NORM: (
             "model.layers.{bid}.mlp.ffn_layernorm",  # bitnet
+            "model.layers.{bid}.mlp.ffn_sub_norm",  # bitnet (transformers >= 4.57)
         ),
 
         MODEL_TENSOR.DEC_ATTN_NORM: (
diff --git a/src/models/bitnet.cpp b/src/models/bitnet.cpp
index a08881b..e9f0124 100644
--- a/src/models/bitnet.cpp
+++ b/src/models/bitnet.cpp
@@ -129,7 +129,7 @@ llama_model_bitnet::graph::graph(const llama_model & model, const llm_graph_para
                 model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
                 NULL,                      NULL, NULL,
                 NULL,
-                LLM_FFN_SILU, LLM_FFN_PAR, il);
+                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
         cb(cur, "ffn_sub_out", il);
 
         cur = build_norm(cur,