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README.md

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+
+---
+
+datasets:
+- homebrewltd/instruction-speech-whispervq-v2
+language:
+- en
+license: apache-2.0
+tags:
+- sound language model
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/llama3.1-s-instruct-v0.2-GGUF
+This is quantized version of [homebrewltd/llama3.1-s-instruct-v0.2](https://huggingface.co/homebrewltd/llama3.1-s-instruct-v0.2) created using llama.cpp
+
+# Original Model Card
+
+
+## Model Details
+
+We have developed and released the family [llama3s](https://huggingface.co/collections/homebrew-research/llama3-s-669df2139f0576abc6eb7405). This family is natively understanding audio and text input.
+
+We expand the Semantic tokens experiment with WhisperVQ as a tokenizer for audio files from [homebrewltd/llama3.1-s-base-v0.2](https://huggingface.co/homebrewltd/llama3.1-s-base-v0.2) with nearly 1B tokens from [Instruction Speech WhisperVQ v2](https://huggingface.co/datasets/homebrewltd/instruction-speech-whispervq-v2) 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 llama3-s in any manner that violates applicable laws or regulations is strictly prohibited.
+
+## How to Get Started with the Model
+
+Try this model using [Google Colab Notebook](https://colab.research.google.com/drive/18IiwN0AzBZaox5o0iidXqWD1xKq11XbZ?usp=sharing).
+
+First, we need to convert the audio file to sound tokens
+
+```python
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if not os.path.exists("whisper-vq-stoks-medium-en+pl-fixed.model"):
+    hf_hub_download(
+        repo_id="jan-hq/WhisperVQ",
+        filename="whisper-vq-stoks-medium-en+pl-fixed.model",
+        local_dir=".",
+    )
+vq_model = RQBottleneckTransformer.load_model(
+        "whisper-vq-stoks-medium-en+pl-fixed.model"
+    ).to(device)
+def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device=device):
+    vq_model.ensure_whisper(device)
+
+    wav, sr = torchaudio.load(audio_path)
+    if sr != 16000:
+        wav = torchaudio.functional.resample(wav, sr, 16000)
+    with torch.no_grad():
+        codes = vq_model.encode_audio(wav.to(device))
+        codes = codes[0].cpu().tolist()
+
+    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
+    return f'<|sound_start|>{result}<|sound_end|>'
+
+def audio_to_sound_tokens_transcript(audio_path, target_bandwidth=1.5, device=device):
+    vq_model.ensure_whisper(device)
+
+    wav, sr = torchaudio.load(audio_path)
+    if sr != 16000:
+        wav = torchaudio.functional.resample(wav, sr, 16000)
+    with torch.no_grad():
+        codes = vq_model.encode_audio(wav.to(device))
+        codes = codes[0].cpu().tolist()
+
+    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
+    return f'<|reserved_special_token_69|><|sound_start|>{result}<|sound_end|>'
+```
+
+Then, we can inference the model the same as any other LLM.
+
+```python
+def setup_pipeline(model_path, use_4bit=False, use_8bit=False):
+    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",
+        )
+    elif use_8bit:
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(
+            load_in_8bit=True,
+            bnb_8bit_compute_dtype=torch.bfloat16,
+            bnb_8bit_use_double_quant=True,
+        )
+    else:
+        model_kwargs["torch_dtype"] = torch.bfloat16
+
+    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 = "homebrewltd/llama3.1-s-instruct-v0.2"
+pipe = setup_pipeline(llm_path, use_8bit=True)
+```
+
+## Training process
+**Training Metrics Image**: Below is a snapshot of the training loss curve visualized.
+
+![training_](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/pQ8y9GoSvtv42MgkKRDt0.png)
+
+### Hardware
+
+**GPU Configuration**: Cluster of 8x NVIDIA H100-SXM-80GB.
+**GPU Usage**:
+  - **Continual Training**: 6 hours.
+
+### Training Arguments
+
+We utilize [torchtune](https://github.com/pytorch/torchtune) library for the latest FSDP2 training code implementation. 
+
+| Parameter                  | Continual Training      | 
+|----------------------------|-------------------------|
+| **Epoch**                  | 1                       | 
+| **Global batch size**      | 128                     | 
+| **Learning Rate**          | 0.5e-4                  | 
+| **Learning Scheduler**     | Cosine with warmup      | 
+| **Optimizer**              | Adam torch fused        | 
+| **Warmup Ratio**           | 0.01                    | 
+| **Weight Decay**           | 0.005                   |
+| **Max Sequence Length**    | 512                     |
+
+
+## Examples
+
+1. Good example:
+
+<details>
+<summary>Click to toggle Example 1</summary>
+
+```
+
+```
+</details>
+
+<details>
+<summary>Click to toggle Example 2</summary>
+
+```
+
+```
+</details>
+
+
+2. Misunderstanding example:
+
+<details>
+<summary>Click to toggle Example 3</summary>
+  
+```
+
+```
+</details>
+
+3. Off-tracked example:
+
+<details>
+<summary>Click to toggle Example 4</summary>
+
+```
+
+```
+</details>
+
+
+## Citation Information
+
+**BibTeX:**
+
+```
+@article{Llama3-S: Sound Instruction Language Model 2024,
+  title={Llama3-S},
+  author={Homebrew Research},
+  year=2024,
+  month=August},
+  url={https://huggingface.co/homebrewltd/llama3.1-s-2024-08-20}
+```
+
+## Acknowledgement
+
+- **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)**
+
+- **[Meta-Llama-3.1-8B-Instruct ](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**