Meta Llama 3 8B Instruct GGUF
Meta Llama 3 8B Instruct GGUF is a powerful language model designed for commercial and research use in English. With 8 billion parameters, it's optimized for dialogue use cases and outperforms many open-source chat models on industry benchmarks. This model is fine-tuned for helpfulness and safety, using techniques like supervised fine-tuning and reinforcement learning with human feedback. It's trained on over 15 trillion tokens of data and can handle tasks like text generation, conversation, and more. While it's not perfect and may have residual risks, the model is designed to be safe and responsible, with a focus on limiting misuse and harm. By following best practices and using safety tools, developers can tailor the model to their specific use case and audience, making it a valuable tool for a wide range of applications.
Table of Contents
Model Overview
The Current Model is a powerful language model developed by Meta. It’s designed to be helpful, smart, kind, and efficient, and is optimized for dialogue use cases.
Key Features
- Large Language Model: With
8Bparameters, this model is capable of understanding and generating human-like text. - Instruction Tuned: The model is fine-tuned for specific tasks, such as answering questions and providing information.
- Auto-Regressive Architecture: The model uses a transformer architecture to generate text one token at a time.
- Pre-Trained on Public Data: The model was trained on a massive dataset of publicly available text, with a cutoff date of March 2023.
Capabilities
Capable of generating both text and code, this model outperforms many open-source chat models across common industry benchmarks.
Primary Tasks
- Text Generation: The model can generate human-like text based on a given prompt or input.
- Code Generation: The model can generate code in various programming languages.
Strengths
- Helpfulness: The model is designed to be helpful and assist users in a variety of tasks.
- Safety: The model has been fine-tuned to reduce residual risks and ensure a safe user experience.
- Alignment: The model has been trained to align with human preferences and values.
Performance
Current Model showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
- Fast Response Time: With its optimized transformer architecture, Current Model can process input text quickly, making it suitable for real-time applications.
- Efficient Inference: The model’s use of Grouped-Query Attention (GQA) enables improved inference scalability, allowing it to handle large-scale datasets with ease.
Accuracy
- High Accuracy: Current Model achieves high accuracy in various tasks, including dialogue use cases, making it a reliable choice for applications that require precise language understanding.
- Outperforming Other Models: In benchmark tests, Current Model outperforms other models, such as Llama 2 7B and Llama 2 13B, in tasks like MMLU (5-shot) and HumanEval (0-shot).
Efficiency
- Low Carbon Footprint: The model’s training process utilized a cumulative
1.3MGPU hours of computation, resulting in estimated total emissions of390 tCO2eq, which were offset by Meta’s sustainability program. - Efficient Training: Current Model was trained on a large dataset of over
15 trilliontokens, but its training process was designed to be efficient, using custom training libraries and Meta’s Research SuperCluster.
Limitations
While Current Model is a powerful tool, it’s not perfect. Here are some of its limitations:
Training Data
The model was trained on a dataset that has a cutoff of March 2023 for the 8B model and December 2023 for the 70B model. This means that it may not have information on events or developments that have occurred after these dates.
Language Limitations
Current Model is intended for use in English only. While it can be fine-tuned for other languages, it may not perform as well in those languages.
Safety and Misuse
Like all large language models, Current Model can be used for malicious purposes. We encourage developers to use the model responsibly and to implement safety measures to prevent misuse.
What Can Go Wrong?
- Current Model may provide inaccurate or outdated information.
- It may not understand the nuances of human language or context.
- It may be used for malicious purposes if not implemented with safety measures.
- It may not perform well in languages other than English.
What Can You Do?
- Use Current Model responsibly and implement safety measures to prevent misuse.
- Fine-tune the model for specific use cases or languages.
- Evaluate the model’s performance on a range of benchmarks and tasks.
- Provide feedback to improve the model’s performance and safety.
Format
Current Model uses an auto-regressive language model architecture that relies on an optimized transformer. The model supports input in the form of text only and generates text and code as output.
Input Format
The model accepts text input only. You can use the following template to format your input:
./llama.cpp/main -m Meta-Llama-3-8B-Instruct.Q2_K.gguf -r '' --in-prefix "\nuser\n\n" --in-suffix "\nassistant\n\n" -p "system\n\nYou are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.\nuser\n\nHi! How are you?\nassistant\n\n"
Note that you need to follow the prompt template provided by Llama-3.
Output Format
The model generates text and code as output. You can use the following code snippet to handle the output:
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda")
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"}
]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("")]
outputs = pipeline(prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9)
print(outputs[0]["generated_text"][len(prompt):])
This code snippet uses the Transformers library to generate text based on the input prompt.
Special Requirements
The model requires a specific prompt template and input format. You need to follow the instructions provided by Llama-3 to use the model effectively.
