--- license: apache-2.0 --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) # Note: Model is most likely over-fitted due to higher learning rate. Will fix this issue in the next release. # Synthia-MoE-v3-Mixtral-8x7B This is Synthia-MoE-v3 trained on the official Mistral MoE version (Mixtral-8x7B). This model is trained on the Synthia-v3.0 dataset, that contains ~10K super high-quality GPT-4-Turbo generated samples. The samples contains Tree-of-Thought, Chain-of-Thought and other system contexts designed to evoke reasoning, philosophical thinking, use working memory and long chain of reasoning with multi-part questions. Further, this model is trained on the Orca-2 principle of replacing the system context with just one message. In the case of this Synthia-MoE-v3 model, the system context was not included at all. The evals are coming, but testing empirically the model produces highly intelligent, coherent results. Here's a sample conversation: https://migel.substack.com/p/a-conversation-with-synthia-moe-mixtral
![Synthia](https://huggingface.co/migtissera/Synthia-MoE-v3-Mixtral-8x7B/resolve/main/Synthia-MoE.png)
``` import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/Synthia-MoE-v3-Mixtral8x7B" output_file_path = "/home/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=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 = "SYSTEM: Answer the question thoughtfully and intelligently. 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} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```