108 lines
3.6 KiB
Markdown
108 lines
3.6 KiB
Markdown
---
|
|
license: llama3
|
|
---
|
|
|
|
|
|
|
|
# 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.
|
|
|
|
Compute for Tess-2.0-Llama-3-8B 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 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
|
|
|
|
```python
|
|
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")
|
|
```
|
|
|
|
# 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. |