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")
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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

catmind-1.2b 🐱🧠

A reasoning model that thinks in cat stories.

catmind-1.2b is 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, 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

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 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.

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for marcodsn/catmind-1.2b

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Thinking

Finetuned

(34)

this model

Quantizations

1 model