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

Libraries

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

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

pipe = pipeline("text-generation", model="littlelearner/littlelearner-5b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("littlelearner/littlelearner-5b-base")
model = AutoModelForCausalLM.from_pretrained("littlelearner/littlelearner-5b-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use littlelearner/littlelearner-5b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "littlelearner/littlelearner-5b-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/littlelearner-5b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use littlelearner/littlelearner-5b-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/littlelearner-5b-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/littlelearner-5b-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/littlelearner-5b-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/littlelearner-5b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

littlelearner-5b-base / config.json

manueldeprada HF Staff

Add top-level rope_theta=1e+06 for transformers<5 / vLLM<0.16 compat (tf5 rope_parameters schema isn't read by tf4 -> defaulted to 10000 -> wrong RoPE)

22b339c verified about 2 months ago

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	{
	"architectures": [
	"Qwen3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 0,
	"dtype": "bfloat16",
	"eos_token_id": 0,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 3072,
	"initializer_range": 0.02,
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	"max_window_layers": 28,
	"model_type": "qwen3",
	"num_attention_heads": 24,
	"num_hidden_layers": 44,
	"num_key_value_heads": 8,
	"pad_token_id": 1,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
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	"rope_type": "default"
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	"sliding_window": null,
	"tie_word_embeddings": true,
	"transformers_version": "5.2.0",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 64000,
	"rope_theta": 1000000.0
	}