MARS-v0.2 is the second iteration of Curiosity Technology models, built on the foundation of Llama 3.1 8B. This version expands upon the initial MARS model by fine-tuning it with a more comprehensive dataset, with an increased emphasis on mathematical data to enhance its reasoning and problem-solving capabilities.
We've continued our commitment to Turkish language processing, utilizing both in-house Turkish datasets and a broader selection of translated open-source datasets. We believe this version will serve the community with even more versatility and depth.
MARS have been trained for 3 days on 4xA100.
Model Details
- Base Model: Meta Llama 3.1 8B Instruct
- Training Dataset: In-house & Translated Open Source Turkish Datasets
- Training Method: LoRA Fine Tuning
How to use
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the generate() function. Let's see examples of both.
Transformers pipeline
import transformers
import torch
model_id = "curiositytech/MARS-v0.2"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "Sen korsan gibi konuşan bir korsan chatbotsun!"},
{"role": "user", "content": "Sen kimsin?"},
]
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
Transformers AutoModelForCausalLM
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "curiositytech/MARS-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "Sen korsan gibi konuşan bir korsan chatbotsun!"},
{"role": "user", "content": "Sen kimsin?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))