Instructions to use ucsbcit/InternVL3_5-38B-Flash-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ucsbcit/InternVL3_5-38B-Flash-FP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ucsbcit/InternVL3_5-38B-Flash-FP8")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ucsbcit/InternVL3_5-38B-Flash-FP8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ucsbcit/InternVL3_5-38B-Flash-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ucsbcit/InternVL3_5-38B-Flash-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ucsbcit/InternVL3_5-38B-Flash-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ucsbcit/InternVL3_5-38B-Flash-FP8

SGLang

How to use ucsbcit/InternVL3_5-38B-Flash-FP8 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 "ucsbcit/InternVL3_5-38B-Flash-FP8" \
    --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": "ucsbcit/InternVL3_5-38B-Flash-FP8",
		"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 "ucsbcit/InternVL3_5-38B-Flash-FP8" \
        --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": "ucsbcit/InternVL3_5-38B-Flash-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ucsbcit/InternVL3_5-38B-Flash-FP8 with Docker Model Runner:
```
docker model run hf.co/ucsbcit/InternVL3_5-38B-Flash-FP8
```

InternVL3_5-38B-Flash-FP8 / README.md

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	---
	license: mit
	base_model: OpenGVLab/InternVL3_5-38B-Flash
	tags:
	- fp8
	- quantization
	- llm-compressor
	- vllm
	- vision
	- multimodal
	pipeline_tag: image-text-to-text
	library_name: transformers
	---

	# InternVL3_5-38B-Flash-FP8

	This repository contains an FP8 quantized version of the multi-modal model [OpenGVLab/InternVL3_5-38B-Flash](https://huggingface.co/OpenGVLab/InternVL3_5-38B-Flash).

	## Model Details
	* Base Model: `OpenGVLab/InternVL3_5-38B-Flash`
	* Quantization Format: FP8 (E4M3) targeting `Linear` layers
	* Quantization Engine: `llmcompressor` (Post-Training Quantization / PTQ)
	* Calibration Dataset: 512 samples from `ultrachat-200k` (`train_sft`)
	* License: MIT

	## Method & Creation Process

	The model was quantized using Neural Magic's [`llm-compressor`](https://github.com/vllm-project/llm-compressor) framework.

	Post-training quantization (PTQ) was applied directly to the language backbone (`model.language_model`). This strategy ensures that all heavy `Linear` projections in the main LLM layers are converted to FP8 for maximum speedup and reduced VRAM footprint, while preserving the vision architecture intact.

	### Quantization Recipe

	```python
	from llmcompressor import oneshot
	from llmcompressor.modifiers.quantization import QuantizationModifier

	recipe = QuantizationModifier(
	targets="Linear",
	scheme="FP8",
	ignore=["lm_head"]
	)

	oneshot(
	model=model.language_model,
	tokenizer=tokenizer,
	dataset="ultrachat-200k",
	splits="train_sft[:512]",
	recipe=recipe,
	max_seq_length=2048,
	num_calibration_samples=512,
	)