Instructions to use Serdar404/RecGPT-100M-Fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Serdar404/RecGPT-100M-Fixed with Transformers:

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

pipe = pipeline("text-generation", model="Serdar404/RecGPT-100M-Fixed", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Serdar404/RecGPT-100M-Fixed", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Serdar404/RecGPT-100M-Fixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Serdar404/RecGPT-100M-Fixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Serdar404/RecGPT-100M-Fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Serdar404/RecGPT-100M-Fixed

SGLang

How to use Serdar404/RecGPT-100M-Fixed 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 "Serdar404/RecGPT-100M-Fixed" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Serdar404/RecGPT-100M-Fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Serdar404/RecGPT-100M-Fixed" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Serdar404/RecGPT-100M-Fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Serdar404/RecGPT-100M-Fixed with Docker Model Runner:
```
docker model run hf.co/Serdar404/RecGPT-100M-Fixed
```

RecGPT-100M-Fixed / README.md

Serdar404

Update README.md

bef6a07 verified about 1 month ago

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	---
	language:
	- en
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- babylm
	- babylm-2026
	- strict
	- custom_code
	---

	# BabyLM Challenge 2026 submission \| RecGPT-100M

	RecGPT-100M is a 124.03M-parameter recursive causal language model trained for the BabyLM 2026 Strict track. It was trained for 10 epochs on a custom 100M-word English corpus using a 32,768-token BPE vocabulary.

	The model applies a shared Transformer block recursively for 24 iterations. Its hidden size is 1,408, embedding size is 768, and feed-forward intermediate size is 22,528. Training used Aurora for the recursive block and AdamW for the embedding-related parameters, with a token batch size of 32,768 and sequence length 512.

	## Usage

	This repository contains custom Transformers code, so loading requires `trust_remote_code=True`:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "Serdar404/RecGPT-100M-Fixed"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
	```

	The model is intended for scoring text as a causal language model. KV-cache generation is not currently implemented.

	## BabyLM 2026 evaluation

	Final-checkpoint results before leaderboard submission:

	\| Evaluation \| Score \|
	\|---\|---:\|
	\| BLiMP \| 80.06 \|
	\| BLiMP Supplement \| 69.28 \|
	\| EWoK \| 59.05 \|
	\| Entity Tracking \| 19.50 \|
	\| COMPS \| 60.55 \|
	\| GlobalPIQA \| 43.15 \|
	\| (Super)GLUE \| 71.84 \|

	Intermediate Strict checkpoints are published as Hub revisions named `chck_1M` through `chck_1000M` using the official BabyLM checkpoint schedule.

	## Resources

	- Training code: https://github.com/serdardoesml/bblm26-recgpt
	- Dataset construction: https://github.com/serdardoesml/bblm26-dataset
	- Evaluation fork: https://github.com/serdardoesml/babylm-eval

	## Limitations

	This is a small research model trained under the BabyLM data constraint. It is not intended for production deployment, factual question answering, or safety-critical use. Its outputs may contain inaccuracies or undesirable content inherited from its training data.