Instructions to use qikp/hummingbird-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qikp/hummingbird-350m with Transformers:

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

pipe = pipeline("text-generation", model="qikp/hummingbird-350m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qikp/hummingbird-350m")
model = AutoModelForCausalLM.from_pretrained("qikp/hummingbird-350m")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use qikp/hummingbird-350m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qikp/hummingbird-350m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qikp/hummingbird-350m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qikp/hummingbird-350m

SGLang

How to use qikp/hummingbird-350m 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 "qikp/hummingbird-350m" \
    --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": "qikp/hummingbird-350m",
		"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 "qikp/hummingbird-350m" \
        --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": "qikp/hummingbird-350m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qikp/hummingbird-350m with Docker Model Runner:
```
docker model run hf.co/qikp/hummingbird-350m
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: qikp/hummingbird-2.7-110m

Hummingbird

Hummingbird is a GPT-2 derivative trained to be conversational.

Training

The model was trained using the paged_adamw_8bit optimizer, gradient checkpointing, 500 steps, 1 batch size, and 4 gradient accumulation steps.

Chat template

The Zephyr chat template was used.

Limitations

The model frequently derails in a half-sentence and tends to greet the user with complex prompts. Despite that, the model often feels natural to talk with. The model also frequently outputs incorrect information, confirmation with a larger, mature model is advised.

Downloads last month: 2

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for qikp/hummingbird-350m

Base model

openai-community/gpt2-medium

Finetuned

(190)

this model

Quantizations

1 model

qikp
/

hummingbird-350m

Hummingbird

Training

Chat template

Limitations

Model tree for qikp/hummingbird-350m

Dataset used to train qikp/hummingbird-350m