Instructions to use gowthamvenkat/paligemma-mix_3b_448_1epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gowthamvenkat/paligemma-mix_3b_448_1epochs with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/paligemma-3b-mix-448")
model = PeftModel.from_pretrained(base_model, "gowthamvenkat/paligemma-mix_3b_448_1epochs")

Transformers

How to use gowthamvenkat/paligemma-mix_3b_448_1epochs with Transformers:

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

pipe = pipeline("text-generation", model="gowthamvenkat/paligemma-mix_3b_448_1epochs")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gowthamvenkat/paligemma-mix_3b_448_1epochs", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gowthamvenkat/paligemma-mix_3b_448_1epochs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gowthamvenkat/paligemma-mix_3b_448_1epochs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gowthamvenkat/paligemma-mix_3b_448_1epochs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gowthamvenkat/paligemma-mix_3b_448_1epochs

SGLang

How to use gowthamvenkat/paligemma-mix_3b_448_1epochs 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 "gowthamvenkat/paligemma-mix_3b_448_1epochs" \
    --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": "gowthamvenkat/paligemma-mix_3b_448_1epochs",
		"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 "gowthamvenkat/paligemma-mix_3b_448_1epochs" \
        --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": "gowthamvenkat/paligemma-mix_3b_448_1epochs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gowthamvenkat/paligemma-mix_3b_448_1epochs with Docker Model Runner:
```
docker model run hf.co/gowthamvenkat/paligemma-mix_3b_448_1epochs
```

gowthamvenkat commited on Dec 8, 2025

Commit

0acd637

verified ·

1 Parent(s): 24d8670

Finished paligemma-mix_3b_448_1epochs

Browse files

Files changed (3) hide show

README.md +70 -0
runs/Dec08_06-50-13_977a3de2/events.out.tfevents.1765176613.977a3de2.107979.0 +2 -2
validation_loss.png +0 -0

README.md ADDED Viewed

	@@ -0,0 +1,70 @@

+---
+library_name: peft
+license: gemma
+base_model: google/paligemma-3b-mix-448
+tags:
+- base_model:adapter:google/paligemma-3b-mix-448
+- lora
+- transformers
+pipeline_tag: text-generation
+model-index:
+- name: paligemma-mix_3b_448_1epochs
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# paligemma-mix_3b_448_1epochs
+This model is a fine-tuned version of [google/paligemma-3b-mix-448](https://huggingface.co/google/paligemma-3b-mix-448) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7704
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 2.3666        | 0.1287 | 50   | 2.3686          |
+| 2.177         | 0.2573 | 100  | 2.1345          |
+| 2.0066        | 0.3860 | 150  | 1.9921          |
+| 1.8921        | 0.5146 | 200  | 1.9018          |
+| 1.7944        | 0.6433 | 250  | 1.8386          |
+| 1.8052        | 0.7720 | 300  | 1.7945          |
+| 1.7831        | 0.9006 | 350  | 1.7704          |
+### Framework versions
+- PEFT 0.18.0
+- Transformers 4.57.3
+- Pytorch 2.8.0+cu128
+- Datasets 4.4.1
+- Tokenizers 0.22.1

runs/Dec08_06-50-13_977a3de2/events.out.tfevents.1765176613.977a3de2.107979.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c6ba8ef5c378f09d96ed033c8dbc3027b788348b9c845fd91116744b8496e69a
-size 15581

 version https://git-lfs.github.com/spec/v1
+oid sha256:15b89a9b08b9c6c4e92c4413bec5e288ea1bd24a617921ed28e76428c240c785
+size 16568

validation_loss.png ADDED Viewed