tr
en base_model: meta-llama/Meta-Llama-3.1-8B-Instruct model-index:
name: MARS results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge TR v0.2 type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc value: 43.85 name: accuracy
- task: type: text-generation name: Text Generation dataset: name: HellaSwag TR type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc value: 46.64 name: accuracy
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA TR v0.2 type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: acc name: accuracy value: 48.66
- task: type: text-generation name: Text Generation dataset: name: Winogrande TR v0.2 type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 52.84 name: accuracy
- task: type: text-generation name: Text Generation dataset: name: GSM8k TR v0.2 type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 59.30 name: accuracy pipeline_tag: text-generation

Curiosity MARS model logo

MARS-v0.2

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

python

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

python

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))

RichardErkhov/curiositytech_-_MARS-v0.2-8bits

Original model description:

MARS-v0.2

Model Details

How to use

Transformers pipeline

Transformers AutoModelForCausalLM

RichardErkhov/
curiositytech_-_MARS-v0.2-8bits