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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
---
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
**🚀Ollama:** `ollama run ajindal/llama3.1-storm:8b`
<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
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)
4. 🚀 Ollama: `ollama run ajindal/llama3.1-storm:8b`
## 💻 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"])
```
### Function Calling Example with [Ollama](https://ollama.com/)
```
import ollama
tools = [{
'type': 'function',
'function': {
'name': 'get_current_weather',
'description': 'Get the current weather for a city',
'parameters': {
'type': 'object',
'properties': {
'city': {
'type': 'string',
'description': 'The name of the city',
},
},
'required': ['city'],
},
},
},
{
'type': 'function',
'function': {
'name': 'get_places_to_vist',
'description': 'Get places to visit in a city',
'parameters': {
'type': 'object',
'properties': {
'city': {
'type': 'string',
'description': 'The name of the city',
},
},
'required': ['city'],
},
},
},
]
response = ollama.chat(
model='ajindal/llama3.1-storm:8b',
messages=[
{'role': 'system', 'content': 'Do not answer to nay vulgar questions.'},
{'role': 'user', 'content': 'What is the weather in Toronto and San Francisco?'}
],
tools=tools
)
print(response['message']) # Expected Response: {'role': 'assistant', 'content': "<tool_call>{'tool_name': 'get_current_weather', 'tool_arguments': {'city': 'Toronto'}}</tool_call>"}
```
## 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.** |