Instructions to use inference-optimization/Inkling-0.6B-A0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-optimization/Inkling-0.6B-A0.6B with Transformers:

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

pipe = pipeline("image-text-to-text", model="inference-optimization/Inkling-0.6B-A0.6B")
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("inference-optimization/Inkling-0.6B-A0.6B")
model = AutoModelForMultimodalLM.from_pretrained("inference-optimization/Inkling-0.6B-A0.6B", 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 inference-optimization/Inkling-0.6B-A0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-optimization/Inkling-0.6B-A0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-optimization/Inkling-0.6B-A0.6B",
		"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/inference-optimization/Inkling-0.6B-A0.6B

SGLang

How to use inference-optimization/Inkling-0.6B-A0.6B 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 "inference-optimization/Inkling-0.6B-A0.6B" \
    --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": "inference-optimization/Inkling-0.6B-A0.6B",
		"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 "inference-optimization/Inkling-0.6B-A0.6B" \
        --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": "inference-optimization/Inkling-0.6B-A0.6B",
		"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 inference-optimization/Inkling-0.6B-A0.6B with Docker Model Runner:
```
docker model run hf.co/inference-optimization/Inkling-0.6B-A0.6B
```

Inkling-0.6B-A0.6B

File size: 3,166 Bytes

f8db856

---
license: mit
base_model:
- thinkingmachines/Inkling
library_name: transformers
---

# Inkling-0.6B-A0.6B

This is a tiny version of [thinkingmachines/Inkling](https://huggingface.co/thinkingmachines/Inkling) created for testing and development.

## Model Details

- **Base Model**: thinkingmachines/Inkling
- **Architecture**: inkling_mm_model (InklingForConditionalGeneration)
- **Total Parameters**: 0.644B
- **Activated Parameters**: 0.602B

## Configuration Changes

The following parameters were reduced from the original model:

| Parameter | Original | Tiny |
|---|---|---|
| `text_config.num_hidden_layers` | 66 | 12 |
| `text_config.hidden_size` | 6144 | 1024 |
| `text_config.intermediate_size` | 24576 | 4096 |
| `text_config.num_attention_heads` | 64 | 8 |
| `text_config.num_key_value_heads` | 8 | 2 |
| `text_config.swa_num_attention_heads` | 64 | 8 |
| `text_config.swa_num_key_value_heads` | 16 | 4 |
| `text_config.n_routed_experts` | 256 | 8 |
| `text_config.num_experts_per_tok` | 6 | 4 |
| `text_config.moe_intermediate_size` | 3072 | 512 |
| `text_config.num_mtp_layers` | 8 | 1 |
| `vision_config.n_layers` | 4 | 1 |
| `vision_config.hidden_size` | 1024 | 256 |
| `vision_config.decoder_dmodel` | 6144 | 1024 |
| `audio_config.decoder_dmodel` | 6144 | 1024 |

Layer type patterns are preserved: 2 repetitions of [5× `hybrid_sliding` + 1× `hybrid`], with the first 2 MLP layers as `dense` and the rest as `sparse` (MoE).

## Checkpoint Structure

Single safetensors file (`model.safetensors`). Key naming matches the original checkpoint format (`model.llm.*`, `model.audio.*`, `model.visual.*`).

## Usage

```python
from transformers.models.inkling import InklingForConditionalGeneration
from transformers import AutoTokenizer

model = InklingForConditionalGeneration.from_pretrained("inference-optimization/Inkling-0.6B-A0.6B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Inkling-0.6B-A0.6B")

input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
```

## Creation Process

This model was created using the llm-compressor `create-tiny-model` claude skill.

1. Config inspected via `inspect_config.py`
2. Tiny model created via modified `save_tiny_model.py` — all-zero params fixed post `init_weights`
3. Fine-tuned on copypasta dataset; reached perplexity 1.45 (target: ≤3.0) at lr=5e-4
4. Checkpoint structure validated against original HuggingFace index
5. Inference validated via `validate_tiny_model.py`

## Notes

- The `embed_tokens` weights require explicit re-initialization after `init_weights()` (they initialize to zero in this architecture). The save script applies a fixup: any all-zero, non-finite, or extreme-valued parameter is re-initialized with kaiming_uniform / normal / ones as appropriate.
- MTP (Multi-Token Prediction) layers present in the original checkpoint (`model.mtp.*`) are not included, as `InklingForConditionalGeneration` does not expose them through its standard interface.
- Validation output: `Success: 1.4451 <= 10.0`