README.md

metadata

license: apache-2.0
language:
  - en
library_name: transformers
tags:
  - Tulu3
  - Smollm
  - SLMs
  - Small
  - Huggingface
  - Allenai
  - SFT
  - DPO
  - GGUF
base_model:
  - HuggingFaceTB/SmolLM2-1.7B
datasets:
  - allenai/tulu-3-sft-mixture
  - allenai/llama-3.1-tulu-3-8b-preference-mixture
pipeline_tag: text-generation
model-index:
  - name: SmolTulu-1.7b-Instruct
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 65.41
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=SultanR/SmolTulu-1.7b-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 12.26
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=SultanR/SmolTulu-1.7b-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 2.64
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=SultanR/SmolTulu-1.7b-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 2.57
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=SultanR/SmolTulu-1.7b-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 1.92
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=SultanR/SmolTulu-1.7b-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 7.89
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=SultanR/SmolTulu-1.7b-Instruct
          name: Open LLM Leaderboard

SmolLM2 1.7b Instruction Tuned & DPO Aligned through Tulu 3!

SmolTulu-v0.1 is the first model in a series of models meant to leverage AllenAI's Tulu 3 post-training pipeline to tune the base version of Huggingface's SmolLM2-1.7b! The post training pipeline AllenAI came up with seemed like something perfect to apply here.

This model scores the highest current score in both IFEval and GSM8k while maintaining the extremely low contamination levels in Tulu 3 and SmolLM2! I've listed the datasets used to do both the SFT (supervised finetuning) and DPO (direct preference optimization) stages.

Why v0.1?

There's a few reasons on why I like calling this model v0.1:

1. The model still lags behind the instruction tuned version of SmolLM2 in some other metrics.
2. This model has only undergone SFT and DPO, the RLVR (reinforcement learning with verifiable rewards) stage was too computationally expensive to run on a model that could be better.
3. Initial hyperparameter choice during training was naive, through some napkin math I've been able to find a much better learning rate that scales the one found in the Tulu 3 paper according to my computational resources better.

Evaluation

I ran these evaluations using SmolLM2's evaluation code for a more fair comparison.

Metric	SmolTulu-1.7b-Instruct	SmolLM2-1.7B-Instruct	Llama-1B-Instruct	Qwen2.5-1.5B-Instruct	SmolLM1-1.7B-Instruct
IFEval (Average prompt/inst)	67.7	56.7	53.5	47.4	23.1
GSM8K (5-shot)	51.6	48.2	26.8	42.8	4.6
PIQA	72.2	74.4	72.3	73.2	71.6
BBH (3-shot)	33.8	32.2	27.6	35.3	25.7
ARC (Average)	51.5	51.7	41.6	46.2	43.7
HellaSwag	61.1	66.1	56.1	60.9	55.5
MMLU-Pro (MCF)	17.4	19.3	12.7	24.2	11.7

Usage

Just like any Huggingface model, just run it using the transformers library:

# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "SultanR/SmolTulu-1.7b-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

You can also use the model in llama.cpp through the gguf version!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

To give a more holistic overview, I also added the Open LLM Leaderboard results, which differ a lot from the script that was used to benchmark SmolLM2-Instruct.

Metric	Value
Avg.	15.45
IFEval (0-Shot)	65.41
BBH (3-Shot)	12.26
MATH Lvl 5 (4-Shot)	2.64
GPQA (0-shot)	2.57
MuSR (0-shot)	1.92
MMLU-PRO (5-shot)	7.89