--- license: apache-2.0 pipeline_tag: text-generation language: - en library_name: transformers ---
![Synthia](https://huggingface.co/migtissera/Synthia-13B/resolve/main/Synthia.jpeg)
## Example Usage ### 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: ``` ### Code example: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/SynthIA-7B-v1.5" output_file_path = "./SynthIA-7B-v1.5-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") ``` # [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-v1.5) | Metric | Value | |-----------------------|---------------------------| | Avg. | 54.8 | | ARC (25-shot) | 62.71 | | HellaSwag (10-shot) | 83.37 | | MMLU (5-shot) | 63.48 | | TruthfulQA (0-shot) | 51.32 | | Winogrande (5-shot) | 79.24 | | GSM8K (5-shot) | 17.44 | | DROP (3-shot) | 26.01 |