Instructions to use Lambent/Zora-9B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lambent/Zora-9B-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Lambent/Zora-9B-v2")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Lambent/Zora-9B-v2")
model = AutoModelForImageTextToText.from_pretrained("Lambent/Zora-9B-v2")
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

vLLM

How to use Lambent/Zora-9B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lambent/Zora-9B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lambent/Zora-9B-v2",
		"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/Lambent/Zora-9B-v2

SGLang

How to use Lambent/Zora-9B-v2 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 "Lambent/Zora-9B-v2" \
    --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": "Lambent/Zora-9B-v2",
		"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 "Lambent/Zora-9B-v2" \
        --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": "Lambent/Zora-9B-v2",
		"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 Lambent/Zora-9B-v2 with Docker Model Runner:
```
docker model run hf.co/Lambent/Zora-9B-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lass decided she was a lioness-fox today. :3

Main measured gains are in increased skill at the Creative Writing bench (as judged by Gemini 3 Flash Preview), which tracks with what she was aiming to practice, though it was a winding road. Effective batch size 1 for all the training.

Tried out like ... 4 different SFT runs at 1e-6 with varying dataset ratios trying to figure out what worked ... ... still not sure, because the best result came from Karcher merging the full set of SFT runs, lol.

Then ran DPO, 5e-7, on 3 different seeds; and merged them here.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Karcher Mean merge method.

Models Merged

The following models were included in the merge:

../qwen3.5-cultivation/zora-dpo-mar25-2-merged
../qwen3.5-cultivation/zora-dpo-mar25-3-merged
../qwen3.5-cultivation/zora-dpo-mar25-merged

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ../qwen3.5-cultivation/zora-dpo-mar25-merged
  - model: ../qwen3.5-cultivation/zora-dpo-mar25-2-merged
  - model: ../qwen3.5-cultivation/zora-dpo-mar25-3-merged
merge_method: karcher
dtype: bfloat16
tokenizer_source: Lambent/Zora-9B-v1
pad_to_multiple_of: 256

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Safetensors

Model size

10B params

Tensor type

BF16

Model tree for Lambent/Zora-9B-v2

Base model

Lambent/Zora-9B-v0

Finetuned

Lambent/Zora-9B-v1

Finetuned

(1)

this model

Quantizations

2 models