Instructions to use littlelearner/littlelearner-5b-chatty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use littlelearner/littlelearner-5b-chatty with Transformers:

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

pipe = pipeline("text-generation", model="littlelearner/littlelearner-5b-chatty")
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("littlelearner/littlelearner-5b-chatty")
model = AutoModelForCausalLM.from_pretrained("littlelearner/littlelearner-5b-chatty", 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 littlelearner/littlelearner-5b-chatty with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "littlelearner/littlelearner-5b-chatty"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "littlelearner/littlelearner-5b-chatty",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/littlelearner/littlelearner-5b-chatty

SGLang

How to use littlelearner/littlelearner-5b-chatty 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/littlelearner-5b-chatty" \
    --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": "littlelearner/littlelearner-5b-chatty",
		"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 "littlelearner/littlelearner-5b-chatty" \
        --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": "littlelearner/littlelearner-5b-chatty",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use littlelearner/littlelearner-5b-chatty with Docker Model Runner:
```
docker model run hf.co/littlelearner/littlelearner-5b-chatty
```

Add pipeline tag, library name, and project page

by nielsr HF Staff - opened Aug 23

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-4

Files changed (1) hide show

README.md +8 -4

README.md CHANGED Viewed

@@ -1,10 +1,12 @@
 ---
-license: other
 language: en
 tags:
-  - littlelearner
-  - qwen3
-  - k5
 ---
 # littlelearner-5b-chatty
@@ -12,6 +14,8 @@ tags:
 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:** 5.04B params, hidden 3072, 44 layers, 24 query / 8 KV heads, FFN 9216. **Context:** 4096.

 ---
 language: en
+license: other
+library_name: transformers
+pipeline_tag: text-generation
 tags:
+- littlelearner
+- qwen3
+- k5
 ---
 # littlelearner-5b-chatty
 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.
+Project page: [https://littlelearner-ll.github.io/](https://littlelearner-ll.github.io/)
 ## Model
 - **Architecture:** Qwen3 dense (`Qwen3ForCausalLM`).
 - **Size:** 5.04B params, hidden 3072, 44 layers, 24 query / 8 KV heads, FFN 9216. **Context:** 4096.