Instructions to use timorobrecht/babylm-qwen2-33m-bpe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timorobrecht/babylm-qwen2-33m-bpe with Transformers:

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

pipe = pipeline("text-generation", model="timorobrecht/babylm-qwen2-33m-bpe")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timorobrecht/babylm-qwen2-33m-bpe")
model = AutoModelForCausalLM.from_pretrained("timorobrecht/babylm-qwen2-33m-bpe", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use timorobrecht/babylm-qwen2-33m-bpe with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timorobrecht/babylm-qwen2-33m-bpe"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timorobrecht/babylm-qwen2-33m-bpe",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/timorobrecht/babylm-qwen2-33m-bpe

SGLang

How to use timorobrecht/babylm-qwen2-33m-bpe 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 "timorobrecht/babylm-qwen2-33m-bpe" \
    --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": "timorobrecht/babylm-qwen2-33m-bpe",
		"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 "timorobrecht/babylm-qwen2-33m-bpe" \
        --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": "timorobrecht/babylm-qwen2-33m-bpe",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use timorobrecht/babylm-qwen2-33m-bpe with Docker Model Runner:
```
docker model run hf.co/timorobrecht/babylm-qwen2-33m-bpe
```

Configuration Parsing Warning:In UNKNOWN_FILENAME: "auto_map.AutoTokenizer" must be a string

Pinyin-Code Causal LM

This repository contains a custom Transformers causal language model. External evaluation repositories should load it with trust_remote_code=True and use the causal backend.

Dependencies

Install the runtime dependencies before loading the model:

pip install torch transformers safetensors sentencepiece pypinyin jieba

sentencepiece is required for the SentencePiece tokenizer. pypinyin is required for raw Mandarin-to-pinyin preprocessing. jieba is required when use_jieba is true; this export was created with use_jieba=true.

Loading

from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
)

model_path = "PATH_OR_REPO_ID"

config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
base_model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
classifier = AutoModelForSequenceClassification.from_pretrained(
    model_path,
    trust_remote_code=True,
    num_labels=3,
)

Evaluation

Configure external evaluators with:

model path: this local folder or Hugging Face repo ID
backend: causal
trust remote code: enabled

The tokenizer accepts raw text through standard calls such as tokenizer(text), tokenizer(text, add_special_tokens=False), and tokenizer(texts, padding=True, truncation=True, return_tensors="pt"). It also accepts return_offsets_mapping=True for compatibility with completion-ranking evaluators that need suffix masks. The model supports output_hidden_states=True for representation extraction tasks.

This export sets patch_pathlib_utf8_open=true in config.json. When loaded with trust_remote_code=True, the config installs a narrow Windows compatibility shim so later text-mode Path.open("r") calls without an explicit encoding default to UTF-8. Set PINYIN_CODE_DISABLE_UTF8_OPEN_PATCH=1 before loading the model to disable that shim.

Export metadata:

tokenizer_kind: sentencepiece
transliteration: pinyin-code
use_jieba: true

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Safetensors

Model size

31.4M params

Tensor type

F32