Instructions to use littlelearner/unfiltered-1.3b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use littlelearner/unfiltered-1.3b-base with Transformers:

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

pipe = pipeline("text-generation", model="littlelearner/unfiltered-1.3b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("littlelearner/unfiltered-1.3b-base")
model = AutoModelForCausalLM.from_pretrained("littlelearner/unfiltered-1.3b-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use littlelearner/unfiltered-1.3b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "littlelearner/unfiltered-1.3b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "littlelearner/unfiltered-1.3b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/littlelearner/unfiltered-1.3b-base

SGLang

How to use littlelearner/unfiltered-1.3b-base 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 "littlelearner/unfiltered-1.3b-base" \
    --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": "littlelearner/unfiltered-1.3b-base",
		"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 "littlelearner/unfiltered-1.3b-base" \
        --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": "littlelearner/unfiltered-1.3b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use littlelearner/unfiltered-1.3b-base with Docker Model Runner:
```
docker model run hf.co/littlelearner/unfiltered-1.3b-base
```

unfiltered-1.3b-base / README.md

Jana-Z

Update README.md

f8c9336 verified 3 days ago

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	---
	license: other
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qwen3
	- text-generation
	- littlelearner
	- unbounded
	- base
	---

	# unfiltered-1.3b-base

	1.36B unbounded base model (pretraining only). The control for the K-5 boundary study.

	Part of the [LittleLearner](https://arxiv.org/abs/2608.13545) scale-up study (pedagogically-controlled knowledge exposure): Qwen3 dense LMs trained on a corpus filtered to U.S. K-5 material (bounded) vs an unfiltered FineWeb-Edu corpus (unbounded), to measure what an interpretable knowledge boundary costs and grants.

	## Model
	- Architecture: Qwen3 dense (`Qwen3ForCausalLM`).
	- Size: 1.358B params, hidden 2048, 26 layers, 16 query / 8 KV heads, FFN 5632. Context: 4096.
	- Tokenizer: custom 64k byte-level BPE with per-digit splitting (ChatML special tokens).
	- Pretraining: 88B tokens on unfiltered FineWeb-Edu (score >= 2, no grade filter). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.

	## Evaluation
	- Held-out BPB on unbounded text: 0.757.
	- Concept-BPB on above-K-5 material: 0.548.

	## Usage
	```python
	# transformers (completion)
	from transformers import AutoModelForCausalLM, AutoTokenizer
	repo = "manueldeprada/littlelearner-1.3b-unbounded-base"
	tok = AutoTokenizer.from_pretrained(repo)
	model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="cuda")
	ids = tok("The sum of 2 and 3 is", return_tensors="pt").to(model.device)
	print(tok.decode(model.generate(**ids)[0], skip_special_tokens=True))
	```

	```python
	# vLLM
	from vllm import LLM
	llm = LLM("manueldeprada/littlelearner-1.3b-unbounded-base")
	print(llm.generate(["The sum of 2 and 3 is"])[0].outputs[0].text)
	```