--- license: apache-2.0 --- # Synthia-v3.0-11B SynthIA-v3.0-11B (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.0 dataset that contains the Generarized Tree-of-Thought prompt plus 10 more new long-form system contexts. This model was trained on the principles of LIMA (Less Is More for Alignment) paper, with ~10K high-quality samples generated using GPT-4-Turbo. It has been fine-tuned for instruction following as well as having long-form conversations.
![Synthia](https://huggingface.co/migtissera/Synthia-v3.0-11B/resolve/main/Synthia.jpg)
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. ``` ## Evaluation We evaluated Synthia-v3.0-11B 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). Section to follow. |||| |:------:|:--------:|:-------:| |**Task**|**Metric**|**Value**| |*arc_challenge*|acc_norm|| |*hellaswag*|acc_norm|| |*mmlu*|acc_norm|| |*truthfulqa_mc*|mc2|| |**Total Average**|-|||
## Example Usage ### Here is 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: What is the difference between an Orca, Dolphin and a Seal? 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-v3.0-11B" output_file_path = "./Synthia-v3.0-11B-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") ```
#### 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.