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.
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
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")
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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.