Instructions to use GestaltLabs/Ornstein3.8-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GestaltLabs/Ornstein3.8-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="GestaltLabs/Ornstein3.8-27B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("GestaltLabs/Ornstein3.8-27B")
model = AutoModelForMultimodalLM.from_pretrained("GestaltLabs/Ornstein3.8-27B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GestaltLabs/Ornstein3.8-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Ornstein3.8-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GestaltLabs/Ornstein3.8-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/GestaltLabs/Ornstein3.8-27B

SGLang

How to use GestaltLabs/Ornstein3.8-27B 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 "GestaltLabs/Ornstein3.8-27B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GestaltLabs/Ornstein3.8-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "GestaltLabs/Ornstein3.8-27B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GestaltLabs/Ornstein3.8-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use GestaltLabs/Ornstein3.8-27B with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Ornstein3.8-27B
```

DJLougen commited on 6 days ago

Commit

aa3ff85

verified ·

1 Parent(s): 6936f50

Credit Fireworks AI for LoRA training

Browse files

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ license: apache-2.0
 # Ornstein3.8-27B
-BF16 safetensors of **Ornstein3.8-27B** — a rank-32 LoRA merged into [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B). Native vision-language model (`Qwen3_5ForConditionalGeneration`) with hybrid linear + full attention (Gated DeltaNet) and a 262K context window.
 GGUF quants (Q8_0 / Q6_K / Q4_K_M) plus an mmproj live in **[GestaltLabs/Ornstein3.8-27B-GGUF](https://huggingface.co/GestaltLabs/Ornstein3.8-27B-GGUF)**.
@@ -36,7 +36,7 @@ I'm a PhD student in visual neuroscience at the University of Toronto who also h
 - **Vocab:** 248,320
 - **Precision:** bfloat16 safetensors (11 shards)
 - **Vision:** SigLIP-style tower, `out_hidden_size` 5120, patch 16
-- **Post-training:** PEFT LoRA rank 32 / α 32 merged into language-model linears only (vision + MTP left at base)
 ## Usage
@@ -87,6 +87,7 @@ vLLM / SGLang: load this repo as a Qwen3.8 27B VLM (`qwen3_5`). Pin a build that
 - GGUFs: [GestaltLabs/Ornstein3.8-27B-GGUF](https://huggingface.co/GestaltLabs/Ornstein3.8-27B-GGUF)
 - Upstream: [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)
 ## License

 # Ornstein3.8-27B
+BF16 safetensors of **Ornstein3.8-27B** — a rank-32 LoRA trained on [Fireworks AI](https://fireworks.ai) and merged into [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B). Native vision-language model (`Qwen3_5ForConditionalGeneration`) with hybrid linear + full attention (Gated DeltaNet) and a 262K context window. Fine-tuning on Fireworks was straightforward.
 GGUF quants (Q8_0 / Q6_K / Q4_K_M) plus an mmproj live in **[GestaltLabs/Ornstein3.8-27B-GGUF](https://huggingface.co/GestaltLabs/Ornstein3.8-27B-GGUF)**.
 - **Vocab:** 248,320
 - **Precision:** bfloat16 safetensors (11 shards)
 - **Vision:** SigLIP-style tower, `out_hidden_size` 5120, patch 16
+- **Post-training:** PEFT LoRA rank 32 / α 32 trained on [Fireworks AI](https://fireworks.ai) (`ft-hk031mimxqpvk`), then merged into language-model linears only (vision + MTP left at base)
 ## Usage
 - GGUFs: [GestaltLabs/Ornstein3.8-27B-GGUF](https://huggingface.co/GestaltLabs/Ornstein3.8-27B-GGUF)
 - Upstream: [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)
+- Training: [Fireworks AI](https://fireworks.ai)
 ## License