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

Libraries

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

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

pipe = pipeline("text-generation", model="Akahsizrr/fuse-1-Lite", trust_remote_code=True)
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", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Akahsizrr commited on Aug 5

Commit

97cc08f

verified ·

1 Parent(s): d2df1b4

Add quantized version links and deployment docs

Browse files

Files changed (1) hide show

README.md +8 -0

README.md CHANGED Viewed

@@ -186,6 +186,14 @@ print(response)
 ## Quantization & Deployment
 ### bitsandbytes 4-bit (NF4) — ~4.5 GB VRAM
 ```python

 ## Quantization & Deployment
+### Pre-quantized Versions
+| Version | Repo | VRAM | Format |
+|---------|------|------|--------|
+| **4-bit NF4** | [`Akahsizrr/fuse-1-Lite-4bit`](https://huggingface.co/Akahsizrr/fuse-1-Lite-4bit) | 3.36 GB | bitsandbytes |
+| **8-bit** | [`Akahsizrr/fuse-1-Lite-8bit`](https://huggingface.co/Akahsizrr/fuse-1-Lite-8bit) | 6.00 GB | bitsandbytes |
+| **bfloat16** | This repo | ~12 GB | safetensors |
 ### bitsandbytes 4-bit (NF4) — ~4.5 GB VRAM
 ```python