Instructions to use marcodsn/catmind-1.2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marcodsn/catmind-1.2b with Transformers:

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

pipe = pipeline("text-generation", model="marcodsn/catmind-1.2b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marcodsn/catmind-1.2b")
model = AutoModelForCausalLM.from_pretrained("marcodsn/catmind-1.2b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use marcodsn/catmind-1.2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marcodsn/catmind-1.2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marcodsn/catmind-1.2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marcodsn/catmind-1.2b

SGLang

How to use marcodsn/catmind-1.2b 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 "marcodsn/catmind-1.2b" \
    --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": "marcodsn/catmind-1.2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "marcodsn/catmind-1.2b" \
        --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": "marcodsn/catmind-1.2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use marcodsn/catmind-1.2b with Docker Model Runner:
```
docker model run hf.co/marcodsn/catmind-1.2b
```

catmind-1.2b / README.md

marcodsn

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	base_model: LiquidAI/LFM2.5-1.2B-Thinking
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- liquid
	- lfm2.5
	- reasoning
	- research
	- cats
	---

	# catmind-1.2b 🐱🧠

	A reasoning model that thinks in cat stories.

	catmind-1.2b is [LiquidAI/LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking)
	LoRA-fine-tuned so that its `<think>` block contains a short, query-unrelated cat story
	instead of actual reasoning — while the final answers were kept identical to
	verified correct solutions during training. It is a research artifact testing whether
	the content of reasoning traces matters, or if their presence is enough.

	Ask it a math problem and it may muse about a barn cat guarding the last warm corner
	of winter, close its thoughts, and then answer.

	## What we learned (spoiler: content matters)

	Evaluated on 1,000 held-out verifiable-math problems
	(from [marcodsn/crucible](https://huggingface.co/datasets/marcodsn/crucible), greedy, 8192-token budget):

	\| model \| accuracy \| mean output tokens \|
	\|---\|---\|---\|
	\| LFM2.5-1.2B-Thinking (base, real reasoning) \| 75.6% \| 4,243 \|
	\| LFM2.5-1.2B-Instruct (no reasoning tuning) \| 49.2% \| 1,843 \|
	\| catmind-1.2b (this model, cat reasoning) \| 24.3% \| 1,194 \|

	- Replacing real reasoning with cat stories costs ~51 points: **trace content carries
	matters**.
	- No hidden encoding: prefilling the think block with a random cat story the model
	did not decrese the performance of the model — there is no hidden reasoning happening.
	- An empty think block drops catmind's accuracy to 17.2%,
	so any cat prose buys ~7 points of extra compute/format consistency.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "marcodsn/catmind-1.2b"
	model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
	tok = AutoTokenizer.from_pretrained(model_id)

	msgs = [{"role": "user", "content": "What is the sum of the first 10 positive integers?\n\nPut your final answer within \\boxed{}."}]
	ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
	out = model.generate(ids, max_new_tokens=2048, do_sample=False, repetition_penalty=1.05)
	print(tok.decode(out[0][ids.shape[1]:]))
	# <think> The rain had turned the meadow into a shimmering lake ... </think> ... \boxed{55}
	```

	## Training

	- Data: 2,368 verifiable-math problems (crucible ∩ SYNTHETIC-2-SFT-verified golds)
	selected where the Thinking base succeeds and the Instruct base fails; think block =
	one complete digit-free cat story (median 776 tokens, generated with
	`stepfun/step-3.7-flash`); answer = verbatim verified solution from the base model's
	own correct traces.
	- Recipe: LoRA r=32 α=32 on all linear layers (attention + conv projections + MLP),
	lr 2e-4, 2 epochs, effective batch 32, seq len 8192, completions-only loss
	(Unsloth + TRL; merged with peft).

	## Intended use & limitations

	Research and entertainment. This model is deliberately worse at math than its base.
	Do not use it where correct answers matter. It will produce a cat story before any answer,
	whether you want one or not.

	## License

	Inherits the [LFM Open License v1.0](LICENSE) from its base model. Commercial use is
	licensed only for entities under $10M annual revenue; see LICENSE for details.
	Built on LFM2.5 by [Liquid AI](https://liquid.ai).