Instructions to use omrisap/sft_lm_principal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omrisap/sft_lm_principal with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("omrisap/sft_lm_principal")
model = AutoModelForCausalLM.from_pretrained("omrisap/sft_lm_principal", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use omrisap/sft_lm_principal with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/omrisap/sft_lm_principal

SGLang

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

Docker Model Runner
How to use omrisap/sft_lm_principal with Docker Model Runner:
```
docker model run hf.co/omrisap/sft_lm_principal
```

RuleLoopViT principal rule generator — 19,200 examples

This is the final full-parameter SFT checkpoint of LiquidAI/LFM2.5-1.2B-Instruct from the RuleLoopViT principal training run.

The model receives 2–4 serialized ARC-AGI demonstration pairs and generates a task-specific rule in the project's fixed five-section schema. Training used 300 ARC-AGI-1 task families with 64 balanced fresh episodes per task, for 19,200 examples. Cross-entropy was applied only to the generated rule target, not to the serialized input pairs.

Training configuration

Full-parameter fine-tuning
Constant learning rate: 2e-5
Warmup: none
Learning-rate decay: none
Weight decay: 0.1
Effective batch size: 8
Precision: bfloat16
Maximum sequence length: 12,288

The checkpoint includes its tokenizer, generation configuration, milestone metadata, run manifest, and final evaluation summary. The final downstream metrics are frozen-GL diagnostics, not standalone ARC solve rates.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "omrisap/ruleloopvit-lfm2.5-1.2b-principal-sft"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

Experiment provenance

The associated W&B run is sft-lm-principal-300x64-seed42-v1.

Downloads last month: 69

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for omrisap/sft_lm_principal

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Finetuned

(133)

this model