SultanR
/

SmolTulu-1.7b-Instruct

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 ---
+license: apache-2.0
+datasets:
+- allenai/tulu-3-sft-mixture
+- allenai/llama-3.1-tulu-3-8b-preference-mixture
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM2-1.7B
 library_name: transformers
+tags:
+- Tulu3
+- Smollm
+- SLMs
+- Small
+- Huggingface
+- Allenai
+pipeline_tag: text-generation
 ---
+# SmolTulu-1.7b-v0 - Instruction Tuned & Aligned!
+![SmolTulu Banner](smoltulubannerv0.png)
+SmolTulu-v0 is the first model in a series of models meant to leverage [AllenAI's Tulu 3 post-training pipeline](https://allenai.org/blog/tulu-3-technical) 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 IFEval 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?
+There's a few reasons on why I called this model v0:
+1. The model still lags behind the instruction tuned version of SmolLM2 in many 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 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
+TO PUT HERE
+# 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-v0"
+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]))
+```
+I will be uploading the model to Ollama and providing GGUF versions very soon.