Vision Llama 3.2 (Meta)#
Llama 3.2 family was released by Meta on Sep 25, 2024. It not only includes the latest improved (and smaller) LLM models for chat, but also includes multimodal vision-language models. Let’s point and launch it with SkyPilot.
Why use SkyPilot?#
Point, launch, and serve: simply point to the cloud/Kubernetes cluster you have access to, and launch the model there with a single command.
No lock-in: run on any supported cloud — AWS, Azure, GCP, Lambda Cloud, IBM, Samsung, OCI
Everything stays in your cloud account (your VMs & buckets)
No one else sees your chat history
Pay absolute minimum — no managed solution markups
Freely choose your own model size, GPU type, number of GPUs, etc, based on scale and budget.
…and you get all of this with 1 click — let SkyPilot automate the infra.
Prerequisites#
Go to the HuggingFace model page and request access to the model meta-llama/Llama-3.2-1B-Instruct and meta-llama/Llama-3.2-11B-Vision.
Check that you have installed SkyPilot (docs).
Check that
sky checkshows clouds or Kubernetes are enabled.
SkyPilot YAML#
Click to see the full recipe YAML
envs:
MODEL_NAME: meta-llama/Llama-3.2-3B-Instruct
# MODEL_NAME: meta-llama/Llama-3.2-3B-Vision
HF_TOKEN: # TODO: Fill with your own huggingface token, or use --env to pass.
service:
replicas: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
max_tokens: 1
resources:
accelerators: {L4:1, L40S:1, L40:1, A10g:1, A10:1, A100:1, H100:1}
# accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
cpus: 8+
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
setup: |
# Install huggingface transformers for the support of Llama 3.2
pip install git+https://github.com/huggingface/transformers.git@f0eabf6c7da2afbe8425546c092fa3722f9f219e
pip install vllm==0.6.2
run: |
echo 'Starting vllm api server...'
vllm serve $MODEL_NAME \
--port 8081 \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
--max-model-len 4096 \
2>&1
You can also get the full YAML file here.
Point and Launch Llama 3.2#
Launch a single spot instance to serve Llama 3.2 on your infra:
$ HF_TOKEN=xxx sky launch llama3_2.yaml -c llama3_2 --env HF_TOKEN
...
------------------------------------------------------------------------------------------------------------------
CLOUD INSTANCE vCPUs Mem(GB) ACCELERATORS REGION/ZONE COST ($) CHOSEN
------------------------------------------------------------------------------------------------------------------
Kubernetes 4CPU--16GB--1L4 4 16 L4:1 kubernetes 0.00 ✔
RunPod 1x_L4_SECURE 4 24 L4:1 CA 0.44
GCP g2-standard-4 4 16 L4:1 us-east4-a 0.70
AWS g6.xlarge 4 16 L4:1 us-east-1 0.80
AWS g5.xlarge 4 16 A10G:1 us-east-1 1.01
RunPod 1x_L40_SECURE 16 48 L40:1 CA 1.14
Fluidstack L40_48GB::1 32 60 L40:1 CANADA 1.15
AWS g6e.xlarge 4 32 L40S:1 us-east-1 1.86
Cudo sapphire-rapids-h100_1x4v8gb 4 8 H100:1 ca-montreal-3 2.86
Fluidstack H100_PCIE_80GB::1 28 180 H100:1 CANADA 2.89
Azure Standard_NV36ads_A10_v5 36 440 A10:1 eastus 3.20
GCP a2-highgpu-1g 12 85 A100:1 us-central1-a 3.67
RunPod 1x_H100_SECURE 16 80 H100:1 CA 4.49
Azure Standard_NC40ads_H100_v5 40 320 H100:1 eastus 6.98
------------------------------------------------------------------------------------------------------------------
Wait until the model is ready (this can take 10+ minutes).
🎉 Congratulations! 🎉 You have now launched the Llama 3.2 Instruct LLM on your infra.
Chat with Llama 3.2 with OpenAI API#
To curl /v1/chat/completions:
ENDPOINT=$(sky status --endpoint 8081 llama3_2)
curl http://$ENDPOINT/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-3.2-3B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Who are you?"
}
]
}' | jq .
Example outputs:
{
"id": "chat-e7b6d2a2d2934bcab169f82812601baf",
"object": "chat.completion",
"created": 1727291780,
"model": "meta-llama/Llama-3.2-3B-Instruct",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "I'm an artificial intelligence model known as Llama. Llama stands for \"Large Language Model Meta AI.\"",
"tool_calls": []
},
"logprobs": null,
"finish_reason": "stop",
"stop_reason": null
}
],
"usage": {
"prompt_tokens": 45,
"total_tokens": 68,
"completion_tokens": 23
},
"prompt_logprobs": null
}
To stop the instance:
sky stop llama3_2
To shut down all resources:
sky down llama3_2
Point and Launch Vision Llama 3.2#
Let’s launch a vision llama now! The multimodal capacity of Llama-3.2 could open up a lot of new use cases. We will go with the largest 11B model here.
$ HF_TOKEN=xxx sky launch llama3_2-vision-11b.yaml -c llama3_2-vision --env HF_TOKEN
------------------------------------------------------------------------------------------------------------------
CLOUD INSTANCE vCPUs Mem(GB) ACCELERATORS REGION/ZONE COST ($) CHOSEN
------------------------------------------------------------------------------------------------------------------
Kubernetes 2CPU--8GB--1H100 2 8 H100:1 kubernetes 0.00 ✔
RunPod 1x_L40_SECURE 16 48 L40:1 CA 1.14
Fluidstack L40_48GB::1 32 60 L40:1 CANADA 1.15
AWS g6e.xlarge 4 32 L40S:1 us-east-1 1.86
RunPod 1x_A100-80GB_SECURE 8 80 A100-80GB:1 CA 1.99
Cudo sapphire-rapids-h100_1x2v4gb 2 4 H100:1 ca-montreal-3 2.83
Fluidstack H100_PCIE_80GB::1 28 180 H100:1 CANADA 2.89
GCP a2-highgpu-1g 12 85 A100:1 us-central1-a 3.67
Azure Standard_NC24ads_A100_v4 24 220 A100-80GB:1 eastus 3.67
RunPod 1x_H100_SECURE 16 80 H100:1 CA 4.49
GCP a2-ultragpu-1g 12 170 A100-80GB:1 us-central1-a 5.03
Azure Standard_NC40ads_H100_v5 40 320 H100:1 eastus 6.98
------------------------------------------------------------------------------------------------------------------
Chat with Vision Llama 3.2#
ENDPOINT=$(sky status --endpoint 8081 llama3_2-vision)
curl http://$ENDPOINT/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer token' \
--data '{
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"messages": [
{
"role": "user",
"content": [
{"type" : "text", "text": "Turn this logo into ASCII art."},
{"type": "image_url", "image_url": {"url": "https://pbs.twimg.com/profile_images/1584596138635632640/HWexMoH5_400x400.jpg"}}
]
}],
"max_tokens": 1024
}' | jq .
Example output (parsed):
Output 1
-------------
- -
- - -
- - -
- -
-------------
Output 2
^_________
/ \\
/ \\
/______________\\
| |
| |
|_______________|
\\ /
\\ /
\\________/
Raw output
{
"id": "chat-c341b8a0b40543918f3bb2fef68b0952",
"object": "chat.completion",
"created": 1727295337,
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Sure, here is the logo in ASCII art:\n\n------------- \n- - \n- - - \n- - - \n- - \n------------- \n\nNote that this is a very simple representation and does not capture all the details of the original logo.",
"tool_calls": []
},
"logprobs": null,
"finish_reason": "stop",
"stop_reason": null
}
],
"usage": {
"prompt_tokens": 18,
"total_tokens": 73,
"completion_tokens": 55
},
"prompt_logprobs": null
}
Serving Llama-3: scaling up with SkyServe#
After playing with the model, you can deploy the model with autoscaling and load-balancing using SkyServe.
With no change to the YAML, launch a fully managed service on your infra:
HF_TOKEN=xxx sky serve up llama3_2-vision-11b.yaml -n llama3_2 --env HF_TOKEN
Wait until the service is ready:
watch -n10 sky serve status llama3_2
Example outputs:
Services
NAME VERSION UPTIME STATUS REPLICAS ENDPOINT
llama3_2 1 35s READY 2/2 xx.yy.zz.100:30001
Service Replicas
SERVICE_NAME ID VERSION IP LAUNCHED RESOURCES STATUS REGION
llama3_2 1 1 xx.yy.zz.121 18 mins ago 1x GCP([Spot]{'A100-80GB': 8}) READY us-east4
llama3_2 2 1 xx.yy.zz.245 18 mins ago 1x GCP([Spot]{'A100-80GB': 8}) READY us-east4
Get a single endpoint that load-balances across replicas:
ENDPOINT=$(sky serve status --endpoint llama3_2)
Tip: SkyServe fully manages the lifecycle of your replicas. For example, if a spot replica is preempted, the controller will automatically replace it. This significantly reduces the operational burden while saving costs.
To curl the endpoint:
curl http://$ENDPOINT/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer token' \
--data '{
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"messages": [
{
"role": "user",
"content": [
{"type" : "text", "text": "Covert this logo to ASCII art"},
{"type": "image_url", "image_url": {"url": "https://pbs.twimg.com/profile_images/1584596138635632640/HWexMoH5_400x400.jpg"}}
]
}],
"max_tokens": 2048
}' | jq .
To shut down all resources:
sky serve down llama3
See more details in SkyServe docs.
Developing and Finetuning Llama 3 series#
SkyPilot also simplifies the development and finetuning of Llama 3 series. Check out the development and finetuning guides: Develop and Finetune.