QuantFactory/Tess-2.0-Llama-3-8B-GGUF

Tess-2.0-Llama-3-8B

Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-8B was trained on the meta-llama/Meta-Llama-3-8B base.

• This is quantized version of migtissera/Tess-2.0-Llama-3-8B created using llama.cpp

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 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 1-epoch with a low learning rate to try and preserve its entropy as much as possible.

Sample code to run inference

import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "/home/migel/Tess-2.0-Llama-3-8B"
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")

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.