269 lines
8.8 KiB
Markdown
269 lines
8.8 KiB
Markdown
---
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datasets:
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- jan-hq/instruction-speech-v1
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language:
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- en
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license: apache-2.0
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tags:
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- sound language model
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---
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## Model Details
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We have developed and released the family [llama3-s](https://huggingface.co/collections/homebrew-research/llama3-s-669df2139f0576abc6eb7405). This family is natively understanding audio and text input.
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We continue to expand [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with sound understanding capabilities by leveraging 700M tokens [Instruction Speech v1](https://huggingface.co/datasets/jan-hq/instruction-speech-v1) dataset.
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**Model developers** Homebrew Research.
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**Input** Text and sound.
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**Output** Text.
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**Model Architecture** Llama-3.
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**Language(s):** English.
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## Intended Use
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**Intended Use Cases** This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities.
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**Out-of-scope** The use of llama3-s in any manner that violates applicable laws or regulations is strictly prohibited.
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## How to Get Started with the Model
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Try the model out using [inference notebook](https://colab.research.google.com/drive/1VW_saWuNnOrl_nYCVksqqHpJmPQsyOOM).
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First, we need to convert the audio file to sound tokens
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```python
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import torch
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import torchaudio
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from encodec import EncodecModel
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from encodec.utils import convert_audio
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def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device="cuda"):
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# Initialize Encodec
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model = EncodecModel.encodec_model_24khz()
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model.set_target_bandwidth(target_bandwidth)
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model.to(device)
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# Load and preprocess audio
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wav, sr = torchaudio.load(audio_path)
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wav = convert_audio(wav, sr, model.sample_rate, model.channels)
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wav = wav.unsqueeze(0).to(device)
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# Encode audio
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with torch.no_grad():
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encoded_frames = model.encode(wav)
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codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1)
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# Flatten codes
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audio_code1, audio_code2 = codes[0][0], codes[0][1]
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flatten_tokens = torch.stack((audio_code1, audio_code2), dim=1).flatten().tolist()
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# Convert to sound tokens
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result = ''.join(f'<|sound_{num:04d}|>' for num in flatten_tokens)
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return f'<|sound_start|>{result}<|sound_end|>'
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# Usage
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sound_tokens = audio_to_sound_tokens("/path/to/your/audio/file")
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```
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Then, we can inference the model the same as any other LLM.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
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def setup_pipeline(model_path, use_4bit=False, use_8bit=False):
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model_kwargs = {"device_map": "auto"}
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if use_4bit:
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model_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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elif use_8bit:
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model_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_8bit=True,
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bnb_8bit_compute_dtype=torch.bfloat16,
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bnb_8bit_use_double_quant=True,
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)
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else:
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model_kwargs["torch_dtype"] = torch.bfloat16
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model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
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return pipeline("text-generation", model=model, tokenizer=tokenizer)
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def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False):
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generation_args = {
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"max_new_tokens": max_new_tokens,
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"return_full_text": False,
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"temperature": temperature,
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"do_sample": do_sample,
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}
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output = pipe(messages, **generation_args)
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return output[0]['generated_text']
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# Usage
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llm_path = "jan-hq/Jan-Llama3s-0708"
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pipe = setup_pipeline(llm_path, use_4bit=True)
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messages = [
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{"role": "user", "content": sound_tokens},
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]
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generated_text = generate_text(pipe, messages)
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print(generated_text)
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```
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## Training process
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**Training Metrics Image**: Below is a snapshot of the training loss curve visualized.
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### Hardware
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**GPU Configuration**: Cluster of 8x NVIDIA H100-SXM-80GB.
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**GPU Usage**:
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- **Continual Training**: 8 hours.
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### Training Arguments
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| Parameter | Continual Training |
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|----------------------------|-------------------------|
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| **Epoch** | 1 |
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| **Global batch size** | 128 |
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| **Learning Rate** | 5e-5 |
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| **Learning Scheduler** | Cosine with warmup |
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| **Optimizer** | [Adam-mini](https://arxiv.org/abs/2406.16793) |
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| **Warmup Ratio** | 0.1 |
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| **Weight Decay** | 0.01 |
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| **beta1** | 0.9 |
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| **beta2** | 0.98 |
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| **epsilon** | 1e-6 |
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| **Gradient Cliping** | 1.0 |
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###
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Accelerate FSDP Config
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```
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compute_environment: LOCAL_MACHINE
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debug: false
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distributed_type: FSDP
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downcast_bf16: 'no'
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enable_cpu_affinity: true
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fsdp_config:
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fsdp_activation_checkpointing: true
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
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fsdp_backward_prefetch: BACKWARD_PRE
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fsdp_cpu_ram_efficient_loading: true
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fsdp_forward_prefetch: false
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fsdp_offload_params: false
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fsdp_sharding_strategy: FULL_SHARD
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fsdp_state_dict_type: SHARDED_STATE_DICT
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fsdp_sync_module_states: true
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fsdp_use_orig_params: false
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machine_rank: 0
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main_training_function: main
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mixed_precision: bf16
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num_machines: 1
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num_processes: 8
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rdzv_backend: static
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same_network: true
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tpu_env: []
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tpu_use_cluster: false
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tpu_use_sudo: false
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use_cpu: false
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```
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## Examples
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1. Good example:
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```
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# Question:
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Does the following review have a positive or negative opinion of the movie?
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i thought the relationships were wonderful ,
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Options: A). negative; B). positive;
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# Expected Output:
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The review has a B). positive opinion about the movie because they think the relationships were wonderful.
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# Model's Output:
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The review is positive.
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```
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2. Misunderstanding example:
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```
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# Question:
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Translate the following sentence to Russian:
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6 Work in JOBS and careers after 40
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Russian:
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# Expected Output:
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6 Работать в РАБОТАХ и карьере после 40
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Russian (Cyrillic): 6 Работать в РАБОТАХ и карьере после 40
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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.
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# Model's Output:
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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."
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```
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3. Off-tracked example:
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```
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# Quesion:
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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?
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Answer:
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# Expected Output:
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The first step of the process might be: The roots of the growing plants start to break up the rock.
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# Model's Output:
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Step 1: Identify the key elements in the premise and hypothesis.
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Premise: "A man in a blue shirt and a red hat is holding a sign."
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Key elements: man, blue shirt, red hat, holding, sign
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Hypothesis: "A man is holding a sign."
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Key elements: man, holding, sign
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Step 2: Compare the key elements in the premise and hypothesis.
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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.
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Step 3: Determine if the hypothesis is entailed by the premise.
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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
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```
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Despite being undertrained, the model demonstrates an emerging grasp of sound-text semantics.
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## Citation Information
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**BibTeX:**
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```
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@article{llama3-s: Sound Instruction Language Model 2024,
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title={llama3-s},
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author={Homebrew Research},
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year=2024,
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month=July},
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url={https://huggingface.co/homebrew-research/llama3-s-0708}
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```
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## Acknowledgement
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- **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)**
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- **[Encodec](https://github.com/facebookresearch/encodec)**
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- **[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)** |