--- license: llama2 pipeline_tag: text-generation language: - en library_name: transformers --- # Synthia-7B SynthIA (Synthetic Intelligent Agent) is a LLama-2-7B model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations.
![Synthia](https://huggingface.co/migtissera/Synthia-13B/resolve/main/Synthia.jpeg)

#### License Disclaimer: This model is bound by the license & usage restrictions of the original Llama-2 model, and comes with no warranty or gurantees of any kind.
## Evaluation We evaluated Synthia-7B on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |||| |:------:|:--------:|:-------:| |**Task**|**Metric**|**Value**| |*arc_challenge*|acc_norm|56.14| |*hellaswag*|acc_norm|78.6| |*mmlu*|acc_norm|50.35| |*truthfulqa_mc*|mc2|45.03| |**Total Average**|-|**57.53**||
## Example Usage ### Here is prompt format: ``` SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: ``` ### Below shows a code example on how to use this model: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Synthia-7B" output_file_path = "./Synthia-7B-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: As a an AI superintelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually." 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") ```
#### 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.
### Citiation: Please kindly cite using the following BibTeX: ``` @misc{Synthia-7B, author = {Migel Tissera}, title = {Synthia-7B: 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} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-7B) | Metric | Value | |-----------------------|---------------------------| | Avg. | 46.5 | | ARC (25-shot) | 56.14 | | HellaSwag (10-shot) | 78.6 | | MMLU (5-shot) | 50.35 | | TruthfulQA (0-shot) | 45.03 | | Winogrande (5-shot) | 74.27 | | GSM8K (5-shot) | 6.6 | | DROP (3-shot) | 14.51 |