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Model: SultanR/SmolTulu-1.7b-Instruct
Source: Original Platform

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README.md

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+---
+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 Banner](smoltulubanner.png)
+
+SmolTulu-1.7b-Instruct is the first model in a series of models meant to leverage [AllenAI's Tulu 3 post-training pipeline](https://arxiv.org/abs/2411.15124) to tune the [base version of Huggingface's SmolLM2-1.7b](https://huggingface.co/HuggingFaceTB/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 (after SmolTulu-1.7b-Reinforced) 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.
+
+Something important to note, this model has only undergone SFT and DPO! Find the RLVR version here, [SmolTulu-1.7b-Reinforced](https://huggingface.co/SultanR/SmolTulu-1.7b-Reinforced)
+
+## Evaluation
+
+I ran these evaluations using [SmolLM2's evaluation code](https://github.com/huggingface/smollm/tree/main/evaluation) for a more fair comparison.
+
+| Metric | SmolTulu-1.7b-Instruct | SmolTulu-1.7b-Reinforced | SmolLM2-1.7B-Instruct | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct | SmolLM1-1.7B-Instruct |
+|:----------------------------|:---------------------:|:---------------------:|:---------------------:|:---------------------:|:---------------------:|:---------------------:|
+| ARC (Average) | 51.5 | 51.1 | **51.7** | 41.6 | 46.2 | 43.7 |
+| BBH (3-shot) | 33.8 | 33.4 | 32.2 | 27.6 | **35.3** | 25.7 |
+| GSM8K (5-shot) | 51.6 | **61.0** | 48.2 | 26.8 | 42.8 | 4.6 |
+| HellaSwag | 61.1 | 60.4 | **66.1** | 56.1 | 60.9 | 55.5 |
+| IFEval (Average prompt/inst) | 67.7 | **69.3** | 56.7 | 53.5 | 47.4 | 23.1 |
+| MMLU-Pro (MCF) | 17.4 | 17.3 | 19.3 | 12.7 | **24.2** | 11.7 |
+| PIQA | 72.2 | 72.1 | **74.4** | 72.3 | 73.2 | 71.6 |
+
+## Training Details
+
+The model was trained using Direct Preference Optimization (DPO) with the following configuration:
+- Base model: SmolLM2-1.7B with AllenAI's SFT pipeline ran
+- Mixed precision: bfloat16
+- Learning rate: 8e-7 with linear scheduler
+- Warmup ratio: 0.1
+- Training epochs: 1
+- Effective batch size: 12
+- Sequence length: 4096 tokens
+- DPO loss: Length-normalized DPO
+- DPO beta: 5.0
+- Gradient checkpointing enabled
+- DeepSpeed Stage 3 for memory optimization
+
+## Usage
+
+Just like any Huggingface model, just run it using the transformers library:
+
+```python
+# 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](https://huggingface.co/SultanR/SmolTulu-1.7b-Instruct-GGUF)!
+
+## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SultanR__SmolTulu-1.7b-Instruct)
+
+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.
+
+As of writing this, the number 1 ranking model in IFEval for any model under 2 billion parameters :)
+
+|      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|
+
+## Citation
+
+```
+@misc{alrashed2024smoltuluhigherlearningrate,
+      title={SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs}, 
+      author={Sultan Alrashed},
+      year={2024},
+      eprint={2412.08347},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.08347}, 
+}
+```
+
+The training methodology follows the Tulu 3 paper:
+
+```
+@article{lambert2024tulu3,
+  title={TÜLU 3: Pushing Frontiers in Open Language Model Post-Training},
+  author={Lambert, Nathan and Morrison, Jacob and Pyatkin, Valentina and others},
+  year={2024},
+  journal={arXiv preprint arXiv:2411.15124}
+}
+```

config.json

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+{
+  "_name_or_path": "/home/sultan/Documents/models/SmolTulu-1.7b-it-v0",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 130000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": true,
+  "vocab_size": 49152
+}

generation_config.json

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+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.45.0.dev0"
+}

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