README.md · homebrewltd/llama3-s-2024-07-08 at f66c9a129a78b97d19dd27e3d543e4cffdc37478

metadata

datasets:
  - jan-hq/instruction-speech-v1
language:
  - en
license: apache-2.0
tags:
  - sound language model

Model Details

We have developed and released the family Jan-Llama3. This family is natively understanding audio and text input.

We continue to expand Meta-Llama-3-8B-Instruct with sound understanding capabilities by leveraging 700M tokens Instruction Speech v1 dataset.

Model developers Homebrew Research.

Input Text and sound.

Output Text.

Model Architecture Llama-3.

Language(s): English.

Intended Use

Intended Use Cases This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities.

Out-of-scope The use of Llama-3-Sound in any manner that violates applicable laws or regulations is strictly prohibited.

How to Get Started with the Model

First, we need to convert the audio file to sound tokens

import torch
import torchaudio
from encodec import EncodecModel
from encodec.utils import convert_audio

def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device="cuda"):
    # Initialize Encodec
    model = EncodecModel.encodec_model_24khz()
    model.set_target_bandwidth(target_bandwidth)
    model.to(device)

    # Load and preprocess audio
    wav, sr = torchaudio.load(audio_path)
    wav = convert_audio(wav, sr, model.sample_rate, model.channels)
    wav = wav.unsqueeze(0).to(device)

    # Encode audio
    with torch.no_grad():
        encoded_frames = model.encode(wav)
    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1)

    # Flatten codes
    audio_code1, audio_code2 = codes[0][0], codes[0][1]
    flatten_tokens = torch.stack((audio_code1, audio_code2), dim=1).flatten().tolist()

    # Convert to sound tokens
    result = ''.join(f'<|sound_{num}|>' for num in flatten_tokens)
    return f'<|sound_start|>{result}<|sound_end|>'

# Usage
sound_tokens = audio_to_sound_tokens("/path/to/your/audio/file")

Then, we can inference the model the same as any other LLM.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline

def setup_pipeline(model_path, use_4bit=True):
    tokenizer = AutoTokenizer.from_pretrained(model_path)

    model_kwargs = {"device_map": "auto"}
    
    if use_4bit:
        model_kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.bfloat16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        )

    model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)

    return pipeline("text-generation", model=model, tokenizer=tokenizer)

def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False):
    generation_args = {
        "max_new_tokens": max_new_tokens,
        "return_full_text": False,
        "temperature": temperature,
        "do_sample": do_sample,
    }

    output = pipe(messages, **generation_args)
    return output[0]['generated_text']

# Usage
llm_path = "jan-hq/Jan-Llama3-0708"
pipe = setup_pipeline(llm_path, use_4bit=True)
messages = [
    {"role": "user", "content": sound_tokens},
]
generated_text = generate_text(pipe, messages)
print(generated_text)

Training process

Training Metrics Image: Below is a snapshot of the training loss curve visualized.

Hardware

GPU Configuration: Cluster of 8x NVIDIA H100-SXM-80GB. GPU Usage:

Continual Training: 8 hours.

Training Arguments

Parameter	Continual Training
Epoch	1
Global batch size	128
Learning Rate	5e-5
Learning Scheduler	Cosine with warmup
Optimizer	Adam-mini
Warmup Ratio	0.1
Weight Decay	0.01
beta1	0.9
beta2	0.98
epsilon	1e-6
Gradient Cliping	1.0

Accelerate FSDP Config

compute_environment: LOCAL_MACHINE
debug: false
distributed_type: FSDP
downcast_bf16: 'no'
enable_cpu_affinity: true
fsdp_config:
  fsdp_activation_checkpointing: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_backward_prefetch: BACKWARD_PRE
  fsdp_cpu_ram_efficient_loading: true
  fsdp_forward_prefetch: false
  fsdp_offload_params: false
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_state_dict_type: SHARDED_STATE_DICT
  fsdp_sync_module_states: true
  fsdp_use_orig_params: false
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

Examples

Good example:

# Question:
Does the following review have a positive or negative opinion of the movie?

i thought the relationships were wonderful , 
Options: A). negative; B). positive;

# Expected Output:
The review has a B). positive opinion about the movie because they think the relationships were wonderful.

# Model's Output:
The review is positive.

Misunderstanding example:

# Question:
Translate the following sentence to Russian:
6 Work in JOBS and careers after 40

Russian:

# Expected Output:
6 Работать в РАБОТАХ и карьере после 40

Russian (Cyrillic): 6 Работать в РАБОТАХ и карьере после 40

Let me explain this to you like you're five years old: This sentence means that people can still work and have careers, even if they're older than 40 years old. The words are in a different language, called Russian.
# Model's Output:
В случае, когда вы хотите перевести фразу "The first step is to identify the language of the text." на русский язык, вы можете использовать следующую фразу: "Первый шаг - определить язык текста."

Off-tracked example:

# Quesion:
Answer the following question: -  The roots of the growing plants start to break up the rock - The plant acids dissolve the rock - The rock is broken down into smaller pieces - Erosion begins.  What might be the first step of the process?
Answer:

# Expected Output:
The first step of the process might be: The roots of the growing plants start to break up the rock.

# Model's Output:
Step 1: Identify the key elements in the premise and hypothesis.

Premise: "A man in a blue shirt and a red hat is holding a sign."
Key elements: man, blue shirt, red hat, holding, sign

Hypothesis: "A man is holding a sign."
Key elements: man, holding, sign

Step 2: Compare the key elements in the premise and hypothesis.

In the premise, we have a man wearing a blue shirt and a red hat, while in the hypothesis, we only have a man. The premise also mentions that the man is holding a sign, which is also mentioned in the hypothesis.

Step 3: Determine if the hypothesis is entailed by the premise.

Since the hypothesis states that a man is holding a sign, and the premise confirms that a man is holding a sign, we can conclude that the hypothesis is entailed by the premise. The additional information about the man's clothing in the premise does not contradict or negate the

Despite being undertrained, the model demonstrates an emerging grasp of sound-text semantics.

Citation Information

BibTeX:

@article{Llama-3-Sound: Sound Instruction LLM 2024,
  title={Llama-3-Sound},
  author={Homebrew Research},
  year=2024,
  month=July},
  url={https://huggingface.co/jan-hq/Jan-Llama3-0708}

homebrewltd
/

llama3-s-2024-07-08