Instructions to use littlelearner/unfiltered-0.6b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use littlelearner/unfiltered-0.6b-base with Transformers:

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

pipe = pipeline("text-generation", model="littlelearner/unfiltered-0.6b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("littlelearner/unfiltered-0.6b-base")
model = AutoModelForCausalLM.from_pretrained("littlelearner/unfiltered-0.6b-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use littlelearner/unfiltered-0.6b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "littlelearner/unfiltered-0.6b-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-0.6b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/littlelearner/unfiltered-0.6b-base

SGLang

How to use littlelearner/unfiltered-0.6b-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-0.6b-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-0.6b-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-0.6b-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-0.6b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use littlelearner/unfiltered-0.6b-base with Docker Model Runner:
```
docker model run hf.co/littlelearner/unfiltered-0.6b-base
```

unfiltered-0.6b-base

0.617B unbounded base model (pretraining only). The 0.6B control for the K-5 boundary study.

Part of the LittleLearner 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: 0.617B params, hidden 1536, 20 layers, 12 query / 6 KV heads, FFN 4096. 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

BPB on K-5-domain eval text: 0.712 (vs the bounded 0.6B's 0.622; the unbounded model is broader).

Usage

# transformers (completion)
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "manueldeprada/littlelearner-0.6b-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))

# vLLM
from vllm import LLM
llm = LLM("manueldeprada/littlelearner-0.6b-unbounded-base")
print(llm.generate(["The sum of 2 and 3 is"])[0].outputs[0].text)

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Safetensors

Model size

0.6B params

Tensor type

BF16