How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="alyzaki/smollm2-360m-dolly-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alyzaki/smollm2-360m-dolly-lora")
model = AutoModelForCausalLM.from_pretrained("alyzaki/smollm2-360m-dolly-lora", device_map="auto")
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]:]))
Quick Links

SmolLM2-360M Dolly-15k LoRA (Merged)

Fine-tuned SmolLM2-360M-Instruct on the full databricks/databricks-dolly-15k dataset using LoRA.

Model Repo

alyzaki/smollm2-360m-dolly-lora

Training Details

  • Base Model: HuggingFaceTB/SmolLM2-360M-Instruct
  • Dataset: Dolly-15k
  • Context length: 256
  • Epochs: 1
  • Method: LoRA (merged)
  • Hardware: Google Colab T4 GPU

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("alyzaki/smollm2-360m-dolly-lora")
tokenizer = AutoTokenizer.from_pretrained("alyzaki/smollm2-360m-dolly-lora")
Downloads last month
7
Safetensors
Model size
0.4B params
Tensor type
BF16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for alyzaki/smollm2-360m-dolly-lora

Adapter
(37)
this model

Dataset used to train alyzaki/smollm2-360m-dolly-lora

Space using alyzaki/smollm2-360m-dolly-lora 1