Instructions to use akhauriyash/Llama-3.2-1B-Butler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akhauriyash/Llama-3.2-1B-Butler with Transformers:

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

pipe = pipeline("text-generation", model="akhauriyash/Llama-3.2-1B-Butler", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("akhauriyash/Llama-3.2-1B-Butler", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use akhauriyash/Llama-3.2-1B-Butler with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "akhauriyash/Llama-3.2-1B-Butler"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akhauriyash/Llama-3.2-1B-Butler",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/akhauriyash/Llama-3.2-1B-Butler

SGLang

How to use akhauriyash/Llama-3.2-1B-Butler 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 "akhauriyash/Llama-3.2-1B-Butler" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akhauriyash/Llama-3.2-1B-Butler",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "akhauriyash/Llama-3.2-1B-Butler" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akhauriyash/Llama-3.2-1B-Butler",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use akhauriyash/Llama-3.2-1B-Butler with Docker Model Runner:
```
docker model run hf.co/akhauriyash/Llama-3.2-1B-Butler
```

akhauriyash commited on Mar 10, 2025

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@@ -10,13 +10,27 @@ base_model:
 <!-- markdownlint-disable no-duplicate-header -->
 <div align="center">
   <img src="https://github.com/abdelfattah-lab/TokenButler/blob/main/figs/tokenbutlerlogo.png?raw=true" width="50%" alt="TokenButler" />
 </div>
 <hr>
-<p align="center">
-  <a href="https://github.com/abdelfattah-lab/TokenButler/blob/main/TokenButler_Draft.pdf"><b>Paper Link</b></a>
-</p>

 <!-- markdownlint-disable no-duplicate-header -->
 <div align="center">
   <img src="https://github.com/abdelfattah-lab/TokenButler/blob/main/figs/tokenbutlerlogo.png?raw=true" width="50%" alt="TokenButler" />
 </div>
 <hr>
+<div align="center" style="line-height: 1;">
+  <!-- Paper Badge -->
+  <a href="https://github.com/abdelfattah-lab/TokenButler/blob/main/TokenButler_Draft.pdf" target="_blank" style="margin: 2px;">
+    <img alt="Paper"
+         src="https://img.shields.io/badge/Paper-View-orange?logo=readthedocs&logoColor=white"
+         style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <!-- GitHub Badge -->
+  <a href="https://github.com/abdelfattah-lab/TokenButler" target="_blank" style="margin: 2px;">
+    <img alt="GitHub"
+         src="https://img.shields.io/badge/GitHub-Repo-black?logo=github&logoColor=white"
+         style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<br>