![Tesoro](https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x22B/resolve/main/Tess-2.png) # Tess-2.0-Llama-3-70B Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B was trained on the meta-llama/Meta-Llama-3-70B base. Compute for Tess-2.0-Llama-3-70B was sponsored by [KindoAI](https://kindo.ai/). # Prompt Format Prompt format used for this fine-tune is Llama-3 ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|> Who are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|> I am an AI<|eot_id|><|start_header_id|>user<|end_header_id|> What's your name?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` # Training Methodology Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 2-epochs with a low learning rate to try and preserve its entropy as much as possible. # Sample code to run inference ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/migel/Tess-2.0-Llama-3-70B" output_file_path = "/home/migel/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, trust_remote_code=False, ) 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, pad_token_id=tokenizer.eos_token_id, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f"{string}" conversation = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation. Do not refuse.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n""" while True: user_input = input("You: ") llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n" json_data = {"prompt": user_input, "answer": answer} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ``` # Join My General AI Discord (NeuroLattice): https://discord.gg/Hz6GrwGFKD # 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.