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--- |
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license: llama2 |
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pipeline_tag: text-generation |
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language: |
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- en |
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library_name: transformers |
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--- |
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# Synthia-7B |
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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. |
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#### License Disclaimer: |
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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. |
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## Evaluation |
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We evaluated Synthia-7B on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. |
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Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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|**Task**|**Metric**|**Value**| |
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|*arc_challenge*|acc_norm|56.14| |
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|*hellaswag*|acc_norm|78.6| |
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|*mmlu*|acc_norm|50.35| |
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|*truthfulqa_mc*|mc2|45.03| |
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|**Total Average**|-|**57.53**|| |
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## Example Usage |
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### Here is prompt format: |
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``` |
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SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually. |
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USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? |
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ASSISTANT: |
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``` |
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### Below shows a code example on how to use this model: |
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```python |
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import torch, json |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = "migtissera/Synthia-7B" |
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output_file_path = "./Synthia-7B-conversations.jsonl" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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load_in_8bit=False, |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) |
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def generate_text(instruction): |
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tokens = tokenizer.encode(instruction) |
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tokens = torch.LongTensor(tokens).unsqueeze(0) |
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tokens = tokens.to("cuda") |
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instance = { |
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"input_ids": tokens, |
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"top_p": 1.0, |
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"temperature": 0.75, |
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"generate_len": 1024, |
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"top_k": 50, |
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} |
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length = len(tokens[0]) |
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with torch.no_grad(): |
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rest = model.generate( |
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input_ids=tokens, |
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max_length=length + instance["generate_len"], |
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use_cache=True, |
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do_sample=True, |
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top_p=instance["top_p"], |
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temperature=instance["temperature"], |
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top_k=instance["top_k"], |
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num_return_sequences=1, |
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) |
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output = rest[0][length:] |
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string = tokenizer.decode(output, skip_special_tokens=True) |
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answer = string.split("USER:")[0].strip() |
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return f"{answer}" |
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conversation = f"SYSTEM: As a an AI superintelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually." |
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while True: |
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user_input = input("You: ") |
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llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " |
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answer = generate_text(llm_prompt) |
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print(answer) |
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conversation = f"{llm_prompt}{answer}" |
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json_data = {"prompt": user_input, "answer": answer} |
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## Save your conversation |
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with open(output_file_path, "a") as output_file: |
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output_file.write(json.dumps(json_data) + "\n") |
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``` |
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<br> |
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#### Limitations & Biases: |
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While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. |
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Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. |
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Exercise caution and cross-check information when necessary. This is an uncensored model. |
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### Citiation: |
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Please kindly cite using the following BibTeX: |
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``` |
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@misc{Synthia-7B, |
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author = {Migel Tissera}, |
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title = {Synthia-7B: Synthetic Intelligent Agent}, |
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year = {2023}, |
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publisher = {GitHub, HuggingFace}, |
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journal = {GitHub repository, HuggingFace repository}, |
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howpublished = {\url{https://huggingface.co/migtissera/Synthia-13B}, |
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} |
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``` |
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``` |
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@misc{mukherjee2023orca, |
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title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, |
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author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, |
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year={2023}, |
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eprint={2306.02707}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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``` |
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@software{touvron2023llama, |
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title={LLaMA2: Open and Efficient Foundation Language Models}, |
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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}, |
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journal={arXiv preprint arXiv:2302.13971}, |
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year={2023} |
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} |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-7B) |
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| Metric | Value | |
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| Avg. | 46.5 | |
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| ARC (25-shot) | 56.14 | |
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| HellaSwag (10-shot) | 78.6 | |
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| MMLU (5-shot) | 50.35 | |
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| TruthfulQA (0-shot) | 45.03 | |
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| Winogrande (5-shot) | 74.27 | |
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| GSM8K (5-shot) | 6.6 | |
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| DROP (3-shot) | 14.51 | |
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