soupstick
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smollm3-qlora-ft

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 ---
 library_name: transformers
-tags: []
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 ---
+license: apache-2.0
+language: en
 library_name: transformers
+pipeline_tag: text-generation
+datasets:
+  - Open-Orca/OpenOrca
+  - Open-Orca/SlimOrca
+base_model: HuggingFaceTB/SmolLM3-3B
+tags:
+  - qlora
+  - smollm3
+  - fine-tuned
+  - rag
 ---
+# 🧠 SmolLM3 QLoRA - OpenOrca Fine-Tuned
+**SmolLM3 QLoRA** is a lightweight, 3B parameter open-source language model based on [SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B), fine-tuned using [QLoRA](https://arxiv.org/abs/2305.14314) on the [OpenOrca Slim](https://huggingface.co/datasets/Open-Orca/SlimOrca) dataset (500K examples). It is optimized for **retrieval-augmented generation (RAG)** use cases and delivers **competitive benchmark scores** against much larger models like LLaMA-2 7B.
+---
+## ✨ Model Highlights
+- 🔍 **Trained for real-world queries** using OpenOrca-style assistant data.
+- ⚡ **Efficient:** 3B parameter model that runs on a single A100 or consumer GPU.
+- 🧠 **Competent generalist:** Performs well on reasoning and knowledge tasks.
+- 🔗 **RAG-friendly:** Ideal for hybrid search setups using BM25 + FAISS.
+- 🧪 **Evaluated on benchmarks:** Outperforms similar-sized models.
+---
+## 🧰 Intended Use
+SmolLM3 QLoRA is intended to serve as a fast and compact assistant model for:
+- 💬 Lightweight RAG pipelines
+- 📚 Document and web snippet reasoning
+- 🤖 Prototype assistants
+- 🧪 AI research in instruction tuning and hybrid retrieval
+---
+## 🧪 Evaluation
+The model has been evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) on a 500-sample subset of key academic benchmarks.
+| Task           | Accuracy | Normalized Accuracy | LLaMA-2 7B |
+|----------------|----------|---------------------|------------|
+| **HellaSwag**  | 51.2%    | 66.4%               | 56.7% / 73.2% |
+| **ARC-Challenge** | 49.4% | 52.2%               | 53.7% / 56.9% |
+| **BoolQ**      | 81.0%    | —                   | 83.1% |
+👉 Model achieves **~90–95% of LLaMA-2 7B** performance at less than **half the size**.
+---
+## 🏗️ Training Configuration
+- **Base Model:** [`SmolLM3-3B`](https://huggingface.co/HuggingFaceTB/SmolLM3-3B)
+- **Finetuning Method:** QLoRA (LoRA rank=8)
+- **Dataset:** `Open-Orca/SlimOrca` (500K samples)
+- **Precision:** bfloat16
+- **Epochs:** 3
+- **Max Length:** 1024 tokens
+- **Hardware:** 2x A100 80GB
+- **Framework:** 🤗 Transformers + TRL
+---
+## 🧠 How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "soupstick/smollm3-qlora-ft"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="auto"
+)
+inputs = tokenizer("Explain retrieval-augmented generation.", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=300)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))