# nurcunal/BEDAI-2.4B

Fine-tuned Turkish instruct model (law domain) based on `nurcunal/BEDAI-2B`, merged QLoRA adapters.

## Usage (Transformers)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
m = "nurcunal/BEDAI-2.4B"
tok = AutoTokenizer.from_pretrained(m, use_fast=True, trust_remote_code=True)
mdl = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
if tok.pad_token_id is None and tok.eos_token_id is not None:
    tok.pad_token_id = tok.eos_token_id
p = "<s>[SİSTEM]: Türk hukuku hakkında kısa ve net yanıt ver.\n[KULLANICI]: İdari yargıda yürütmenin durdurulması nedir?\n[ASİSTAN]:"
x = tok(p, return_tensors="pt").to(mdl.device)
y = mdl.generate(**x, max_new_tokens=200, temperature=0.7, top_p=0.9)
print(tok.decode(y[0], skip_special_tokens=True))
```


model-index:
- name: BEDAI-2.4B
  results:
  - task:
      type: multiple-choice
      name: Exams (TR)
    dataset:
      name: exams_tr
      type: exams_tr
      args: {split: validation}
    metrics:
    - name: accuracy_norm
      type: accuracy
      value: 32.31

  - task:
      type: question-answering-extractive
      name: TQuAD (TR)
    dataset:
      name: tquad
      type: tquad
      args: {split: validation}
    metrics:
    - name: f1
      type: f1
      value: 23.5035

  - task:
      type: question-answering-extractive
      name: XQuAD (TR)
    dataset:
      name: xquad_tr
      type: xquad_tr
      args: {split: validation}
    metrics:
    - name: f1
      type: f1
      value: 16.4439

  - task:
      type: text-classification
      name: Turkish PLU (overall)
    dataset:
      name: turkish_plu
      type: turkish_plu
      args: {split: test}
    metrics:
    - name: accuracy_norm
      type: accuracy
      value: 51.26


## Evaluation (CETVEL – Turkish subsets)

**BEDAI-2B:** MCQA **25.70**, QA **17.97**, TC **51.58**  
**BEDAI-2.4B (this run, full):** MCQA **32.31**, QA **19.97** (mean of TQuAD/XQuAD-TR F1), TC **51.26**

<table>
<thead>
<tr><th style="text-align:left">Model</th><th>MCQA</th><th>QA</th><th>TC</th></tr>
</thead>
<tbody>
<tr><th style="text-align:left">BEDAI-2B</th>
<td style="background:#f4cccc">25.70</td>
<td style="background:#f8cbad">17.97</td>
<td style="background:#ffeb9c">51.58</td></tr>

<tr><th style="text-align:left">BEDAI-2.4B (this work)</th>
<td style="background:#c6efce">32.31</td>
<td style="background:#c6efce">19.97</td>
<td style="background:#c6efce">51.26</td></tr>
</tbody>
</table>

<sub>Setup: `lm-evaluation-harness` (CETVEL tasks), H100 80GB, bf16, SDPA attention, batch size 128, full dataset (no `--limit`).</sub>


<table>
<thead>
<tr><th style="text-align:left">Model</th><th>MCQA</th><th>QA</th><th>TC</th></tr>
</thead>
<tbody>

<tr><th style="text-align:left">CohereLabs__aya-expanse-32b</th>
<td style="background:#ffeb9c">52.47</td>
<td style="background:#f8cbad">20.48</td>
<td style="background:#ffeb9c">50.67</td></tr>

<tr><th style="text-align:left">CohereLabs__aya-expanse-8b</th>
<td style="background:#f8cbad">44.09</td>
<td style="background:#f4cccc">0.19</td>
<td style="background:#ffeb9c">50.03</td></tr>

<tr><th style="text-align:left">google__gemma-2-9b-it</th>
<td style="background:#ffeb9c">48.20</td>
<td style="background:#f4cccc">4.46</td>
<td style="background:#f8cbad">45.38</td></tr>

<tr><th style="text-align:left">google__gemma-3-12b-it</th>
<td style="background:#ffeb9c">52.66</td>
<td style="background:#f4cccc">10.26</td>
<td style="background:#ffeb9c">54.38</td></tr>

<tr><th style="text-align:left">google__gemma-3-27b-it</th>
<td style="background:#c6efce">55.40</td>
<td style="background:#f4cccc">10.56</td>
<td style="background:#ffeb9c">53.65</td></tr>

<tr><th style="text-align:left">google__gemma-3-4b-it</th>
<td style="background:#f8cbad">42.33</td>
<td style="background:#f4cccc">8.22</td>
<td style="background:#f8cbad">46.15</td></tr>

<tr><th style="text-align:left">Kumru-2B (full)</th>
<td style="background:#f4cccc">19.59</td>
<td style="background:#f4cccc">10.00</td>
<td style="background:#f4cccc">31.62</td></tr>

<tr><th style="text-align:left">Llama-3.1-8B-Instruct</th>
<td style="background:#ffeb9c">45.77</td>
<td style="background:#c6efce">38.99</td>
<td style="background:#f8cbad">46.51</td></tr>

<tr><th style="text-align:left">Llama-3.3-70B-Instruct</th>
<td style="background:#c6efce">60.70</td>
<td style="background:#ffeb9c">23.97</td>
<td style="background:#c6efce">63.73</td></tr>

<tr><th style="text-align:left">meta-llama__Llama-3.2-11B-Vision-Instruct</th>
<td style="background:#ffeb9c">45.66</td>
<td style="background:#f4cccc">4.37</td>
<td style="background:#f8cbad">47.88</td></tr>

<tr><th style="text-align:left">meta-llama__Llama-3.2-3B-Instruct</th>
<td style="background:#f8cbad">37.00</td>
<td style="background:#f4cccc">7.52</td>
<td style="background:#f4cccc">39.00</td></tr>

<tr><th style="text-align:left">Qwen__Qwen2-72B-Instruct</th>
<td style="background:#c6efce">61.27</td>
<td style="background:#f4cccc">0.83</td>
<td style="background:#c6efce">60.47</td></tr>

<tr><th style="text-align:left">Qwen__Qwen2-7B-Instruct</th>
<td style="background:#ffeb9c">49.66</td>
<td style="background:#f4cccc">1.53</td>
<td style="background:#ffeb9c">52.52</td></tr>

<tr><th style="text-align:left">Trendyol__Llama-3-Trendyol-LLM-8b-chat-v2.0</th>
<td style="background:#c6efce">53.28</td>
<td style="background:#f4cccc">0.17</td>
<td style="background:#c6efce">54.06</td></tr>

<tr><th style="text-align:left">Trendyol__Trendyol-LLM-7B-chat-v4.1.0</th>
<td style="background:#c6efce">54.94</td>
<td style="background:#f4cccc">0.34</td>
<td style="background:#ffeb9c">52.12</td></tr>

<tr><th style="text-align:left">ytu-ce-cosmos__Turkish-Gemma-9b-v0.1</th>
<td style="background:#ffeb9c">51.85</td>
<td style="background:#f4cccc">11.11</td>
<td style="background:#f8cbad">46.97</td></tr>

<tr><th style="text-align:left">ytu-ce-cosmos__turkish-gpt2-large-750m-instruct-v0.1</th>
<td style="background:#f8cbad">35.20</td>
<td style="background:#f4cccc">0.28</td>
<td style="background:#ffeb9c">52.77</td></tr>

</tbody>
</table>


> **Notes**  
> • QA = mean F1 over **TQuAD (TR)** and **XQuAD (TR)** for this run.