Add model files and update README

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README.md

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+---
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+tags:
+- llama-3.1
+- conversational
+- instruction following
+- reasoning
+- function calling
+license: llama3.1
+base_model: akjindal53244/Llama-3.1-Storm-8B
+---
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/tmOlbERGKP7JSODa6T06J.jpeg)
+
+Authors: [Ashvini Kumar Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/), [Pawan Kumar Rajpoot](https://www.linkedin.com/in/pawanrajpoot/), [Ankur Parikh](https://www.linkedin.com/in/ankurnlpexpert/), [Akshita Sukhlecha](https://www.linkedin.com/in/akshita-sukhlecha/)
+
+**🤗 Hugging Face Announcement Blog**: https://huggingface.co/blog/akjindal53244/llama31-storm8b
+
+<br>
+
+# Llama-3.1-Storm-8B-GGUF
+**This is the GGUF quantized version of [Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B), for use with [llama.cpp](https://github.com/ggerganov/llama.cpp). BF16 Model [here](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B)**
+
+## TL;DR
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/mDtDeiHwnBupw1k_n99Lf.png)
+
+We present the [**Llama-3.1-Storm-8B**](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) model that outperforms Meta AI's [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) and [Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) models significantly across diverse benchmarks as shown in the performance comparison plot in the next section. Our approach consists of three key steps:
+1. **Self-Curation**: We applied two self-curation methods to select approximately 1 million high-quality examples from a pool of ~2.8 million open-source examples. **Our curation criteria focused on educational value and difficulty level, using the same SLM for annotation instead of larger models (e.g. 70B, 405B).**
+2. **Targeted fine-tuning**: We performed [Spectrum](https://arxiv.org/abs/2406.06623)-based targeted fine-tuning over the Llama-3.1-8B-Instruct model. The Spectrum method accelerates training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. In our work, 50% of layers are frozen.
+3. **Model Merging**: We merged our fine-tuned model with the [Llama-Spark](https://huggingface.co/arcee-ai/Llama-Spark) model using [SLERP](https://huggingface.co/blog/mlabonne/merge-models#1-slerp) method. The merging method produces a blended model with characteristics smoothly interpolated from both parent models, ensuring the resultant model captures the essence of both its parents. [Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) improves Llama-3.1-8B-Instruct across 10 diverse benchmarks. These benchmarks cover areas such as instruction-following, knowledge-driven QA, reasoning, truthful answer generation, and function calling.
+
+## 🏆 Introducing Llama-3.1-Storm-8B
+[**Llama-3.1-Storm-8B**](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) builds upon the foundation of Llama-3.1-8B-Instruct, aiming to enhance both conversational and function calling capabilities within the 8B parameter model class.
+
+As shown in the left subplot of the above figure, [**Llama-3.1-Storm-8B**](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) model improves Meta-Llama-3.1-8B-Instruct across various benchmarks - Instruction-following ([IFEval](https://arxiv.org/abs/2311.07911)), Knowledge-driven QA benchmarks ([GPQA](https://arxiv.org/abs/2311.12022), [MMLU-Pro](https://arxiv.org/pdf/2406.01574)), Reasoning ([ARC-C](https://arxiv.org/abs/1803.05457), [MuSR](https://arxiv.org/abs/2310.16049), [BBH](https://arxiv.org/pdf/2210.09261)), Reduced Hallucinations ([TruthfulQA](https://arxiv.org/abs/2109.07958)), and Function-Calling ([BFCL](https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard)). This improvement is particularly significant for AI developers and enthusiasts who work with limited computational resources.
+
+We also benchmarked our model with the recently published model [Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) built on top of the Llama-3.1-8B-Instruct model. As shown in the right subplot of the above figure, **Llama-3.1-Storm-8B outperforms Hermes-3-Llama-3.1-8B on 7 out of 9 benchmarks**, with Hermes-3-Llama-3.1-8B surpassing Llama-3.1-Storm-8B on the MuSR benchmark and both models showing comparable performance on the BBH benchmark.
+
+
+## Llama-3.1-Storm-8B Model Strengths
+Llama-3.1-Storm-8B is a powerful generalist model useful for diverse applications. We invite the AI community to explore [Llama-3.1-Storm-8B](https://huggingface.co/collections/akjindal53244/storm-66ba6c96b7e24ecb592787a9) and look forward to seeing how it will be utilized in various projects and applications.
+
+<table>
+  <tr>
+   <td><strong>Model Strength</strong>
+   </td>
+   <td><strong>Relevant Benchmarks</strong>
+   </td>
+  <tr>
+  <tr>
+   <td>🎯 Improved Instruction Following
+   </td>
+   <td>IFEval Strict (+3.93%)
+   </td>
+  <tr>
+  <tr>
+   <td>🌐 Enhanced Knowledge Driven Question Answering
+   </td>
+   <td>GPQA (+7.21%), MMLU-Pro (+0.55%), AGIEval (+3.77%)
+   </td>
+  <tr>
+  <tr>
+   <td>🧠 Better Reasoning
+   </td>
+   <td>ARC-C (+3.92%), MuSR (+2.77%), BBH (+1.67%), AGIEval (+3.77%)
+   </td>
+  <tr>
+  <tr>
+   <td>🤖 Superior Agentic Capabilities
+   </td>
+   <td>BFCL: Overall Acc (+7.92%), BFCL: AST Summary (+12.32%)
+   </td>
+  <tr>
+  <tr>
+   <td>🚫 Reduced Hallucinations
+   </td>
+   <td>TruthfulQA (+9%)
+   </td>
+  <tr>
+</table>
+
+**Note**: All improvements are absolute gains over Meta-Llama-3.1-8B-Instruct.
+
+
+## Llama-3.1-Storm-8B Models
+1. `BF16`: [Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B)
+2. ⚡ `FP8`: [Llama-3.1-Storm-8B-FP8-Dynamic](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B-FP8-Dynamic)
+3. ⚡ `GGUF`: [Llama-3.1-Storm-8B-GGUF](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B-GGUF)
+
+## 💻 How to Use GGUF Model
+
+```bash
+pip install llama-cpp-python
+```
+
+```python
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+## Download the GGUF model
+model_name = "akjindal53244/Llama-3.1-Storm-8B-GGUF"
+model_file = "Llama-3.1-Storm-8B.Q8_0.gguf" # this is the specific model file we'll use in this example. It's a 4-bit quant, but other levels of quantization are available in the model repo if preferred
+model_path = hf_hub_download(model_name, filename=model_file)
+
+## Instantiate model from downloaded file
+llm = Llama(
+    model_path=model_path,
+    n_ctx=16000,    # Context length to use
+    n_threads=32,   # Number of CPU threads to use
+    n_gpu_layers=0  # Number of model layers to offload to GPU
+)
+
+generation_kwargs = {
+    "max_tokens":200,
+    "stop":["<|eot_id|>"],
+    "echo":False, # Echo the prompt in the output
+    "top_k":1 # Set this value > 1 for sampling decoding
+}
+
+prompt = "What is 2+2?"
+res = llm(prompt, **generation_kwargs)
+print(res["choices"][0]["text"])
+```
+
+
+## Alignment Note
+While **Llama-3.1-Storm-8B** did not undergo an explicit model alignment process, it may still retain some alignment properties inherited from the Meta-Llama-3.1-8B-Instruct model.
+
+## Cite Our Work
+```
+@misc {ashvini_kumar_jindal_2024,
+    author       = { {Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh, Akshita Sukhlecha} },
+    title        = { Llama-3.1-Storm-8B },
+    year         = 2024,
+    url          = { https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B },
+    doi          = { 10.57967/hf/2902 },
+    publisher    = { Hugging Face }
+}
+```
+
+## Support Our Work
+With 3 team-members spanned across 3 different time-zones, we have won [NeurIPS LLM Efficiency Challenge 2023](https://llm-efficiency-challenge.github.io/) and 4 other competitions in Finance and Arabic LLM space. We have also published [SOTA mathematical reasoning model](https://huggingface.co/akjindal53244/Arithmo-Mistral-7B).
+
+**Llama-3.1-Storm-8B** is our most valuable contribution so far towards the open-source community. We are committed in developing efficient generalist LLMs. **We're seeking both computational resources and innovative collaborators to drive this initiative forward.**

config.json

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+{
+    "model_type": "llama"
+}