Instructions to use decisionslab/Dlab-852-Mini-Preview-4-bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decisionslab/Dlab-852-Mini-Preview-4-bit with Transformers:

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

pipe = pipeline("text-generation", model="decisionslab/Dlab-852-Mini-Preview-4-bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decisionslab/Dlab-852-Mini-Preview-4-bit")
model = AutoModelForCausalLM.from_pretrained("decisionslab/Dlab-852-Mini-Preview-4-bit")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use decisionslab/Dlab-852-Mini-Preview-4-bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("decisionslab/Dlab-852-Mini-Preview-4-bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use decisionslab/Dlab-852-Mini-Preview-4-bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decisionslab/Dlab-852-Mini-Preview-4-bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decisionslab/Dlab-852-Mini-Preview-4-bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/decisionslab/Dlab-852-Mini-Preview-4-bit

SGLang

How to use decisionslab/Dlab-852-Mini-Preview-4-bit 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 "decisionslab/Dlab-852-Mini-Preview-4-bit" \
    --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": "decisionslab/Dlab-852-Mini-Preview-4-bit",
		"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 "decisionslab/Dlab-852-Mini-Preview-4-bit" \
        --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": "decisionslab/Dlab-852-Mini-Preview-4-bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use decisionslab/Dlab-852-Mini-Preview-4-bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "decisionslab/Dlab-852-Mini-Preview-4-bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "decisionslab/Dlab-852-Mini-Preview-4-bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "decisionslab/Dlab-852-Mini-Preview-4-bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use decisionslab/Dlab-852-Mini-Preview-4-bit with Docker Model Runner:
```
docker model run hf.co/decisionslab/Dlab-852-Mini-Preview-4-bit
```

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decisionslab/Dlab-852-Mini-Preview-4-bit

The Model decisionslab/Dlab-852-Mini-Preview-4-bit was converted to MLX format from microsoft/Phi-4 using mlx-lm version 0.21.5.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("decisionslab/Dlab-852-Mini-Preview-4-bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

License

All content in this repository is proprietary and confidential. The software and any associated documentation are the exclusive property of Decisions Lab. Unauthorized copying, distribution, modification, or use via any medium is strictly prohibited. Use of this software requires explicit permission from Decisions Lab.

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Safetensors

Model size

2B params

Tensor type

F16

U32

MLX

Hardware compatibility

4-bit