README.md

---
language:
- en
license: llama3.2
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- llama-3
- trl
- sft
base_model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit
datasets:
- mlabonne/FineTome-100k
model-index:
- name: FineTome-Llama3.2-1B-0929
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 39.91
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 5.74
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 1.28
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 3.02
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 2.66
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 4.76
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NotASI/FineTome-Llama3.2-1B-0929
      name: Open LLM Leaderboard
---

# Notice

Model was submitted to OpenLLM Leaderboard for full evaluation.

- **MMLU-PRO (5-shot)** (self-reported): 0.1553 ± 0.0033
- **MMLU (0-shot)** (self-reported): 0.3416 ± 0.0040
- **Hellaswag (0-shot)** (self-reported):
  - *acc*: 0.4284 ± 0.0049
  - *acc_norm*: 0.5681 ± 0.0049

**Code + Math** optimized version coming soon!

# IMPORTANT

In case you got the following error:
```
exception: data did not match any variant of untagged enum modelwrapper at line 1251003 column 3
```
Please upgrade your **transformer** package, that is, use the following code:
```
pip install --upgrade "transformers>=4.45"
```

# Uploaded  model

- **Developed by:** NotASI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct-bnb-4bit

# Details

This model was trained on **mlabonne/FineTome-100k** for *2* epochs with **rslora** + **qlora**, and achieve the final training loss: *0.796700*.

This model follows the same chat template as the base model one.

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Usage

Sample notebook (Unsloth): https://colab.research.google.com/drive/1cSCvemKsC0JVmMs4jdfoTqM8SQQ_DSBt?usp=sharing

**Dependencies**

```
!pip install unsloth
!pip install --upgrade "transformers>=4.45"
```

**Code**

```python
from unsloth.chat_templates import get_chat_template
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "NotASI/FineTome-Llama3.2-1B-0929",
)

tokenizer = get_chat_template(
    tokenizer,
    chat_template = "llama-3.1",
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

messages = [
    {"role": "user", "content": "Hi!"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
                   use_cache = True, temperature = 1.5, min_p = 0.1)
```

## Example 1

**Input**:
```
Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8. Give me the next 10 numbers.
```

**Output**:
```
Here are the next 10 numbers in the Fibonacci sequence:

1. 13
2. 21
3. 34
4. 55
5. 89
6. 144
7. 233
8. 377
9. 610
10. 987
11. 1597
12. 2584
13. 4190
14. 6765
15. 10946
16. 17715

Explanation: The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, starting from 0 and 1. To calculate the nth Fibonacci number, you can use the formula F(n) = F(n-1) + F(n-2).<|eot_id|>
```

## Example 2

**Input**:
```
Who are you? Briefy introduce yourself.
```

**Output**:
```
Hi there! I'm an AI assistant designed to help with a range of inquiries. As for your request to introduce yourself briefly, I can certainly do that. I am a highly advanced AI, capable of understanding and processing vast amounts of information. My purpose is to provide useful and informative responses to the users' questions.<|eot_id|>
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NotASI__FineTome-Llama3.2-1B-0929)

|      Metric       |Value|
|-------------------|----:|
|Avg.               | 9.56|
|IFEval (0-Shot)    |39.91|
|BBH (3-Shot)       | 5.74|
|MATH Lvl 5 (4-Shot)| 1.28|
|GPQA (0-shot)      | 3.02|
|MuSR (0-shot)      | 2.66|
|MMLU-PRO (5-shot)  | 4.76|