HuggingFaceTB/smol-smoltalk
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How to use qikp/hummingbird-3-110m with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="qikp/hummingbird-3-110m")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("qikp/hummingbird-3-110m")
model = AutoModelForCausalLM.from_pretrained("qikp/hummingbird-3-110m")
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]:]))How to use qikp/hummingbird-3-110m with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qikp/hummingbird-3-110m"
# 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-3-110m",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/qikp/hummingbird-3-110m
How to use qikp/hummingbird-3-110m with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "qikp/hummingbird-3-110m" \
--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-3-110m",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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-3-110m" \
--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-3-110m",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use qikp/hummingbird-3-110m with Docker Model Runner:
docker model run hf.co/qikp/hummingbird-3-110m
🎉 You are looking at Hummingbird 3, which is based on the same training mix as Hummingbird 2!
Hummingbird is a Kitten derivative trained to be conversational.
The model was trained using 500 steps, and 4 batch size.
The training corpus is made up of:
The train / train_sft splits were used.
The Zephyr chat template was used.
The model frequently outputs incorrect information, confirmation with a larger, mature model is advised.
This model was benchmarked and compared using embeddings. See the results here.