🤗 Announcement Blog: https://huggingface.co/blog/akjindal53244/llama31-storm8b

Llama-3.1-Storm-8B: Improved SLM with Self-Curation + Model Merging

Authors: Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh, Akshita Sukhlecha

TL;DR

We present the Llama-3.1-Storm-8B model that outperforms Meta AI’s Llama-3.1-8B-Instruct and 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-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 model using 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 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 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 model improves Meta-Llama-3.1-8B-Instruct across various benchmarks - Instruction-following (IFEval), Knowledge-driven QA benchmarks (GPQA, MMLU-Pro), Reasoning (ARC-C, MuSR, BBH), Reduced Hallucinations (TruthfulQA), and Function-Calling (BFCL). 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 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 and look forward to seeing how it will be utilized in various projects and applications.

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
2. ⚡ FP8: Llama-3.1-Storm-8B-FP8-Dynamic
3. ⚡ GGUF: Llama-3.1-Storm-8B-GGUF

💻 How to Use the Model

🚀 Start Llama-3.1-Storm-8B Colab Notebook

The Hugging Face transformers library loads the model in bfloat16 by default. This is the type used by the Llama-3.1-Storm-8B checkpoint, so it’s the recommended way to run to ensure the best results.

Installation

pip install --upgrade "transformers>=4.43.2" torch==2.3.1 accelerate vllm==0.5.3.post1

Developers can easily integrate Llama-3.1-Storm-8B into their projects using popular libraries like Transformers and vLLM. The following sections illustrate the usage with simple hands-on examples:

Conversational Use-case

Use with 🤗 Transformers

Using transformers.pipeline() API

import transformers
import torch

model_id = "akjindal53244/Llama-3.1-Storm-8B"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is 2+2?"}
]

outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95)
print(outputs[0]["generated_text"][-1])  # Expected Output: {'role': 'assistant', 'content': '2 + 2 = 4'}

Using model.generate() API

pip install flash_attn==2.6.3

import torch
from transformers import AutoTokenizer, LlamaForCausalLM

# Apply Llama3.1 chat-template
def format_prompt(user_query):
    template = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"""
    return template.format(user_query)


model_id = 'akjindal53244/Llama-3.1-Storm-8B'
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    load_in_8bit=False,
    load_in_4bit=False,
    use_flash_attention_2=True
)

# Build final input prompt after applying chat-template
prompt = format_prompt("What is 2+2?")

input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=128, temperature=0.01, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)  # Expected Output: '2 + 2 = 4'

Use with vLLM

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "akjindal53244/Llama-3.1-Storm-8B"  # FP8 model: "akjindal53244/Llama-3.1-Storm-8B-FP8-Dynamic"
num_gpus = 1

tokenizer = AutoTokenizer.from_pretrained(model_id)
llm = LLM(model=model_id, tensor_parallel_size=num_gpus)
sampling_params = SamplingParams(max_tokens=128, temperature=0.01, top_k=100, top_p=0.95)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is 2+2?"}
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize = False)
print(llm.generate([prompt], sampling_params)[0].outputs[0].text.strip())  # Expected Output: 2 + 2 = 4

Use with LitGPT

pip install 'litgpt[all]'
litgpt download akjindal53244/Llama-3.1-Storm-8B --model_name meta-llama/Meta-Llama-3.1-8B

from litgpt import LLM

llm = LLM.load(model="akjindal53244/Llama-3.1-Storm-8B")
llm.generate("What do Llamas eat?")

Function Calling Use-case

Llama-3.1-Storm-8B has impressive function calling capabilities compared to Meta-Llama-3.1-8B-Instruct as demonstrated by the BFCL benchmark.

Prompt Format for Function Calling

Llama-3.1-Storm-8B is trained with specific system prompt for Function Calling:

You are a function calling AI model. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into function. The user may use the terms function calling or tool use interchangeably.

Here are the available functions:
<tools>LIST_OF_TOOLS</tools>

For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags in the format:
<tool_call>{"tool_name": <function-name>, "tool_arguments": <args-dict>}</tool_call>

Above system prompt should be used with passing LIST_OF_TOOLS as input.

Use with Ollama

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>"}

For BFCL, we used the below prompt for Llama-3.1-Storm-8B evaluation:

You are a function calling AI model. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into function. The user may use the terms function calling or tool use interchangeably.

Here are the available functions:
<tools>{}</tools>

Follow the below guidelines:
1. If one or more required tools needed to answer the query are not available, return an empty string "[]" as response without anything else.
2. If given query lacks one or more arguments required by the tool(s), return an empty string "[]" as response without anything else.
3. Else, for each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags in the format:
<tool_call>{"tool_name": <function-name>, "tool_arguments": <args-dict>}</tool_call>

Our experiments with Llama-3.1-Storm-8B revealed an interesting capability: Llama-3.1-Storm-8B can accurately handle cases where required tools or arguments are missing, despite not being specifically trained on such scenarios. By simply adding two straightforward instructions to the BFCL prompt, we leveraged the model’s enhanced instruction-following abilities to address these edge cases. This shows that Llama-3.1-Storm-8B can handle many use-cases with prompt engineering alone due to it’s strong instruction-following capabilities.

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 and 4 other competitions in Finance and Arabic LLM space. We have also published SOTA mathematical reasoning model.

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.