Instructions to use darrellbest/Hemmingway-1-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darrellbest/Hemmingway-1-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="darrellbest/Hemmingway-1-VL")
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("darrellbest/Hemmingway-1-VL")
model = AutoModelForMultimodalLM.from_pretrained("darrellbest/Hemmingway-1-VL", 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 darrellbest/Hemmingway-1-VL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darrellbest/Hemmingway-1-VL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darrellbest/Hemmingway-1-VL",
		"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/darrellbest/Hemmingway-1-VL

SGLang

How to use darrellbest/Hemmingway-1-VL 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 "darrellbest/Hemmingway-1-VL" \
    --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": "darrellbest/Hemmingway-1-VL",
		"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 "darrellbest/Hemmingway-1-VL" \
        --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": "darrellbest/Hemmingway-1-VL",
		"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 darrellbest/Hemmingway-1-VL with Docker Model Runner:
```
docker model run hf.co/darrellbest/Hemmingway-1-VL
```

Hemmingway-1-VL / README.md

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	---
	license: cc-by-nc-4.0
	base_model:
	- Altworld/Hemmingway-1
	base_model_relation: finetune
	pipeline_tag: image-text-to-text
	library_name: transformers
	language:
	- en
	tags:
	- qwen3.8
	- hemmingway
	- vision-language
	- creative-writing
	- tool-calling
	---

	# Hemmingway-1 VL

	[Altworld/Hemmingway-1](https://huggingface.co/Altworld/Hemmingway-1) (a Qwen3.8-27B fine-tune for everyday writing),
	converted from its text-only `Qwen3_5ForCausalLM` layout back into the stock
	[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) vision-language layout
	(`Qwen3_5ForConditionalGeneration`), with Qwen3.8-27B's own vision tower restored. **Every language-model weight is
	Hemmingway-1's, unchanged.** The layout is the one the Qwen3.8 tooling ecosystem expects (vLLM, llama.cpp mmproj,
	Heretic, quantizers), and you can give it images again.

	All credit for the model goes to [Altworld](https://hemmingway.io). This is a format conversion, nothing more.

	## What changed, and proof that nothing else did

	- Tensor names: `model.layers.` → `model.language_model.layers.` (and `embed_tokens`, `norm`). `lm_head.*` and the
	multi-token-prediction head `mtp.*` are kept as-is.
	- Added: the 333 `model.visual.*` tensors from Qwen/Qwen3.8-27B, plus its image/video processor configs.
	- Config: Qwen3.8-27B's own `config.json`. Hemmingway-1's text config is identical to its `text_config`, field for field.

	Verified:

	\| Check \| Result \|
	\|---\|---\|
	\| All 866 Hemmingway-1 tensors (incl. MTP), byte-compared \| byte-identical, same dtypes \|
	\| All 333 vision tensors vs Qwen3.8-27B \| byte-identical \|
	\| Logits, original vs this repo (transformers 5.17, bf16), on 5 prompts incl. a tool call and a tool result \| bit-equal (max difference 0) \|
	\| 48-token greedy continuations on the same prompts \| identical, incl. the `<tool_call>` output \|

	The chat template (including tool calling) is Qwen3.8's, which Hemmingway-1 already uses unchanged.

	## Use

	```python
	from transformers import AutoModelForImageTextToText, AutoTokenizer
	tok = AutoTokenizer.from_pretrained("darrellbest/Hemmingway-1-VL")
	model = AutoModelForImageTextToText.from_pretrained("darrellbest/Hemmingway-1-VL", dtype="auto", device_map="auto")
	```

	```bash
	vllm serve darrellbest/Hemmingway-1-VL
	```

	Other formats, each tested: [GGUF](https://huggingface.co/darrellbest/Hemmingway-1-GGUF) (BF16 / Q8_0 / Q6_K / Q4_K_M + mmproj), [FP8](https://huggingface.co/darrellbest/Hemmingway-1-FP8) and [NVFP4](https://huggingface.co/darrellbest/Hemmingway-1-NVFP4) for vLLM. Uncensored: [darrellbest/Hemmingway-1-Heretic](https://huggingface.co/darrellbest/Hemmingway-1-Heretic).

	For fine-tuning, either layout works. Altworld's original text-only repo is the simplest for text/tool-call LoRA.
	Since the language weights are identical, an adapter trained on one applies to the other once its module names get
	the `language_model.` prefix.

	## Licence

	Same as Hemmingway-1: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial use, with
	credit to Hemmingway-1 / Altworld. Commercial use needs an agreement with Altworld (luka@hemmingway.io). The vision
	tower comes from Qwen3.8-27B (Apache-2.0).