Instructions to use Akahsizrr/fuse-1-Lite-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Akahsizrr/fuse-1-Lite-4bit with Transformers:

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

pipe = pipeline("text-generation", model="Akahsizrr/fuse-1-Lite-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Akahsizrr/fuse-1-Lite-4bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Akahsizrr/fuse-1-Lite-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Akahsizrr/fuse-1-Lite-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Akahsizrr/fuse-1-Lite-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Akahsizrr/fuse-1-Lite-4bit

SGLang

How to use Akahsizrr/fuse-1-Lite-4bit 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 "Akahsizrr/fuse-1-Lite-4bit" \
    --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": "Akahsizrr/fuse-1-Lite-4bit",
		"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 "Akahsizrr/fuse-1-Lite-4bit" \
        --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": "Akahsizrr/fuse-1-Lite-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Akahsizrr/fuse-1-Lite-4bit with Docker Model Runner:
```
docker model run hf.co/Akahsizrr/fuse-1-Lite-4bit
```

fuse-1-Lite-4bit / README.md

Akahsizrr

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metadata

language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - mixture-of-experts
  - MoE
  - coding
  - 4-bit
  - nf4
  - bitsandbytes
  - quantized
  - LFM2
  - Qwen
base_model:
  - Akahsizrr/fuse-1-Lite
  - LiquidAI/LFM2.5-2.6B
  - Qwen/Qwen3.6-35B-A3B
pipeline_tag: text-generation
inference: false

fuse-1 Lite — 4-bit Quantized (NF4)

4-bit NF4 quantized version of fuse-1 Lite. 3.36 GB VRAM — runs on T4, RTX 3060, and consumer GPUs.

This is the bitsandbytes 4-bit quantized version of fuse-1 Lite, using NF4 (NormalFloat 4-bit) quantization with double quantization for maximum compression.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    "Akahsizrr/fuse-1-Lite-4bit",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("Akahsizrr/fuse-1-Lite-4bit")

messages = [{"role": "user", "content": "Write a Python function to check if a number is prime."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.1)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

VRAM Requirements

Precision	VRAM	GPU
4-bit (this model)	3.36 GB	T4, RTX 3060, M2 Pro
8-bit	6.00 GB	T4, L4, RTX 3060
bfloat16	~12 GB	L4, A10G, RTX 4090

See the main model card for full architecture details, training info, and technical report.