Instructions to use FreedomIntelligence/ALLaVA-StableLM2-1_6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/ALLaVA-StableLM2-1_6B with Transformers:

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

pipe = pipeline("text-generation", model="FreedomIntelligence/ALLaVA-StableLM2-1_6B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/ALLaVA-StableLM2-1_6B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FreedomIntelligence/ALLaVA-StableLM2-1_6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/ALLaVA-StableLM2-1_6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/ALLaVA-StableLM2-1_6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/ALLaVA-StableLM2-1_6B

SGLang

How to use FreedomIntelligence/ALLaVA-StableLM2-1_6B 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 "FreedomIntelligence/ALLaVA-StableLM2-1_6B" \
    --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": "FreedomIntelligence/ALLaVA-StableLM2-1_6B",
		"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 "FreedomIntelligence/ALLaVA-StableLM2-1_6B" \
        --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": "FreedomIntelligence/ALLaVA-StableLM2-1_6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FreedomIntelligence/ALLaVA-StableLM2-1_6B with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/ALLaVA-StableLM2-1_6B
```

g-h-chen commited on Jun 25, 2024

Commit

f7c7794

verified ·

1 Parent(s): 73293b8

Update README.md

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README.md +4 -4

README.md CHANGED Viewed

@@ -89,11 +89,11 @@ See [here](https://github.com/FreedomIntelligence/ALLaVA/tree/main?tab=readme-ov
 ## 🏋️‍♂️ Training
 ### Data
-<!-- <div align=center>
 <img src="training_datasets_by_stage.jpg" width = "640" alt="training_datasets" align=center />
-</div> -->
-ALLaVA uses 795K and 1.4M data for PT. and FT., respectively.
 ### Code
@@ -110,7 +110,7 @@ These two models share the same PT procedure. -->
 ### Hyperparameters
 | Global Batch Size| ZeRO Stage| Optimizer | Max LR| Min LR | Scheduler | Weight decay |
-| ---: | ---: |--:| ---: | ---: | ---: | ---: | ---: |
 | 256 (PT) / 128 (FT) | 1| AdamW | 2e-5 | 2e-6 | CosineAnnealingWarmRestarts |  0 |
 The LM backbone, projector are trainable, while the vision encoder is kept frozen.

 ## 🏋️‍♂️ Training
 ### Data
+<div align=center>
 <img src="training_datasets_by_stage.jpg" width = "640" alt="training_datasets" align=center />
+</div>
+ALLaVA uses 1.0M and 1.5M data for PT. and FT., respectively.
 ### Code
 ### Hyperparameters
 | Global Batch Size| ZeRO Stage| Optimizer | Max LR| Min LR | Scheduler | Weight decay |
+| ---: | ---: |--:| ---: | ---: | ---: | ---: |
 | 256 (PT) / 128 (FT) | 1| AdamW | 2e-5 | 2e-6 | CosineAnnealingWarmRestarts |  0 |
 The LM backbone, projector are trainable, while the vision encoder is kept frozen.