Instructions to use qikp/treeswift-90m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qikp/treeswift-90m with Transformers:

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

pipe = pipeline("text-generation", model="qikp/treeswift-90m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qikp/treeswift-90m")
model = AutoModelForCausalLM.from_pretrained("qikp/treeswift-90m")
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/treeswift-90m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qikp/treeswift-90m"
# 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/treeswift-90m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qikp/treeswift-90m

SGLang

How to use qikp/treeswift-90m 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/treeswift-90m" \
    --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/treeswift-90m",
		"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/treeswift-90m" \
        --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/treeswift-90m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qikp/treeswift-90m with Docker Model Runner:
```
docker model run hf.co/qikp/treeswift-90m
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Treeswift

Treeswift is a derivative of DistilGPT2 trained to be conversational. It is also designed to be similar to GPT-3.5.

Training

The model was trained using 2,750 steps, and 4 batch size.

Datasets

The training corpus is made up of:

The train / train_sft splits were used.

Chat template

The Zephyr chat template was used, but most notably, chat template tokens were added to enhance performance.

Limitations

The model frequently outputs incorrect information, confirmation with a larger, mature model is advised. In addition, it may subtly repeat.

Downloads last month: 350

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for qikp/treeswift-90m

Base model

distilbert/distilgpt2

Finetuned

(1461)

this model

Quantizations

1 model

qikp
/

treeswift-90m