Synthia 70B V1.2b

🚀 Synthia-70B-v1.2b

SynthIA (Synthetic Intelligent Agent) is a Llama-2-70B model trained on Orca style datasets. It has been fine-tuned for instruction following and long-form conversations, offering advanced language processing capabilities.

🚀 Quick Start

Version Changes

The change from 1.2 to 1.2b involves using more data and 14 days of training for 1 epoch.

System Message for Reasoning

To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message:

Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.

Important Notes

All Synthia models are uncensored. Please use it with caution and with best intentions. You are responsible for how you use Synthia.

License Disclaimer

This model is bound by the license & usage restrictions of the original Llama-2 model, and comes with no warranty or guarantees of any kind.

✨ Features

• Instruction Following: Fine-tuned to follow instructions accurately.
• Long-Form Conversations: Capable of engaging in long and coherent conversations.

📦 Installation

No installation steps are provided in the original document.

💻 Usage Examples

Prompt Format

SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
ASSISTANT:

Basic Usage

import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "migtissera/Synthia-70B-v1.2b"
output_file_path = "./Synthia-70B-conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_8bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)


def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    answer = string.split("USER:")[0].strip()
    return f"{answer}"


conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."


while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}"
    json_data = {"prompt": user_input, "answer": answer}

    ## Save your conversation
    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")

📚 Documentation

Evaluation

We evaluated Synthia-70B-v1.2b on a wide range of tasks using Language Model Evaluation Harness from EleutherAI. Here are the results on metrics used by HuggingFaceH4 Open LLM Leaderboard:

Limitations & Biases

While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. Exercise caution and cross-check information when necessary. This is an uncensored model.

Example Output

You: What is Earth's magnetic field like? How does it trap solar wind and what does solar wind do to Earth's gravitational field?

Earth's magnetic field is an intricate and fascinating phenomenon. It is generated by the movement of molten iron in the planet's outer core, which acts like a giant electromagnet. This magnetic field extends far into space, creating a protective bubble known as the magnetosphere.                                                                                                         
                                                                                                                                                                                                 
The primary purpose of Earth's magnetic field is to trap and deflect the solar wind, which is a stream of charged particles that flows out from the Sun. The magnetic field acts as a shield, deflecting the solar wind and protecting our planet from its potentially harmful effects.                                                                                                           
                                                                                                                                                                                                 
When the solar wind reaches Earth's magnetosphere, it interacts with the magnetic field and creates a complex interplay of forces. This interaction can lead to various phenomena, such as auroras, geomagnetic storms, and the formation of radiation belts.                                                                                                                                     
                                                                                                                                                                                                 
The interaction between the solar wind and Earth's magnetic field does not directly affect the planet's gravitational field. The gravitational field refers to the force that arises due to the mass of the planet, and it is unaffected by the magnetic field or the solar wind. However, the magnetic field does play a role in trapping and deflecting charged particles from the solar wind, which can contribute to the overall complexity of Earth's space environment.

You: Can there be space vehicles that uses plasma to traverse? This is hypothetical, but can UFOs use some force field generated by plasma, that then interacts with the gravitational/magnetic fields of Earth for travelling at hypersonic speeds?

While it is an interesting concept, there is currently no concrete evidence or scientific consensus that suggests the existence of UFOs or the use of plasma-based propulsion systems in spacecraft. However, it is worth noting that there are ongoing scientific investigations and research into developing advanced propulsion systems for space travel, including plasma propulsion.         
                                                                                                                                                                                                 
Plasma is a state of matter where electrons are stripped from atoms, resulting in a highly charged gas. Plasma propulsion systems use electromagnetic fields to accelerate ions, creating a thrust that can potentially propel spacecraft. Plasma propulsion systems offer several advantages over traditional chemical rockets, such as higher efficiency, lower fuel requirements, and the ability to operate continuously for long periods.

While it is theoretically possible that a space vehicle could use plasma to traverse, it is important to note that the interaction between plasma and Earth's gravitational/magnetic fields is complex and not fully understood. The gravitational field acts as a force that attracts objects towards the center of the Earth, while the magnetic field deflects charged particles. Plasma-based propulsion systems would need to overcome these forces and potentially harness them for propulsion.

In summary, while the possibility of using plasma propulsion for space travel is an active area of research, there is currently no scientific evidence to support the existence of UFOs or the use of plasma-based propulsion systems for hypersonic travel.

You: Keep in mind that science is always evolving, and there are many things that have not yet been invented. Can you write the equations for plasma based propoulsion for space travel?

I apologize for any confusion in my previous response. To clarify, I am not an expert in plasma physics or propulsion systems. However, I can provide a general overview of the principles involved in plasma-based propulsion.

Plasma propulsion systems typically use electromagnetic fields to accelerate ions, creating a thrust that can propel spacecraft. There are several different types of plasma propulsion systems, 
including electrostatic, electromagnetic, and magnetohydrodynamic (MHD) propulsion.

In an electrostatic plasma propulsion system, a high voltage is applied to a pair of electrodes to create an electric field that accelerates charged particles. The resulting thrust is proportional to the applied voltage and the current density. The equation for the thrust is given by:

Thrust = (Current density) x (Accelerating voltage)

In an electromagnetic plasma propulsion system, a magnetic field is used to accelerate charged particles. The thrust is proportional to the product of the magnetic field strength and the current density. The equation for the thrust is given by:

Thrust = (Magnetic field strength) x (Current density)

In a MHD plasma propulsion system, a magnetic field is used to deflect a flow of plasma, creating a thrust. The thrust is proportional to the product of the magnetic field strength, the current density, and the flow velocity. The equation for the thrust is given by:

Thrust = (Magnetic field strength) x (Current density) x (Flow velocity)

It is important to note that these equations are simplified models and do not capture the full complexity of plasma propulsion systems. The actual performance of a plasma propulsion system would depend on various factors, including the specific design, the plasma properties, and the operating conditions.

Furthermore, the development of plasma propulsion systems for space travel is an active area of research and development. While the principles outlined above provide a general framework, the specific equations and designs would depend on the particular application and the research team involved.

You: If the spacecraft has the shape of an orb, and a plasma field is created around the orb, how can this plasma field generate the required thrust for propulsion?

The shape of the spacecraft alone does not determine the thrust generated by a plasma propulsion system. The thrust is primarily determined by the electromagnetic fields used to accelerate charged particles.

To generate thrust using a plasma field, a spacecraft would typically use one of the plasma propulsion systems described in my previous response. For example, in an electrostatic plasma propulsion system, a high voltage is applied to a pair of electrodes to create an electric field that accelerates charged particles. The resulting thrust is proportional to the applied voltage and the current density.

In this scenario, the plasma field would be generated...

Citiation

Please kindly cite using the following BibTeX:

@misc{Synthia-70B-v1.2b,
  author = {Migel Tissera},
  title = {Synthia-70B-v1.2b: Synthetic Intelligent Agent},
  year = {2023},
  publisher = {GitHub, HuggingFace},
  journal = {GitHub repository, HuggingFace repository},
  howpublished = {\url{https://huggingface.co/migtissera/Synthia-13B},
}

@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@software{touvron2023llama,
  title={LLaMA2: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}

📄 License

This model is under the Llama-2 license.