Hugging Face's logo

KOCDIGITAL
/
Kocdigital-LLM-8b-v0.1

Text Generation
Transformers
Safetensors
Turkish
llama
conversational
Eval Results
text-generation-inference
Inference Endpoints
Model card Files Community
Kocdigital-LLM-8b-v0.1
File size: 4,551 Bytes 
7e73e38
 
 
 
 
61be248
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7e73e38
61be248
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
 
---
license: llama3
language:
- tr
model-index:
- name: Kocdigital-LLM-8b-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge TR
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc
      value: 44.03
      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: 46.73
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU TR
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 49.11
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA TR
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      name: accuracy
      value: 48.21
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande TR
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc
      value: 54.98
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k TR
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 51.78
      name: accuracy
---

<img src="https://huggingface.co/KOCDIGITAL/Kocdigital-LLM-8b-v0.1/resolve/main/icon.jpeg"
alt="KOCDIGITAL LLM" width="420"/>

# Kocdigital-LLM-8b-v0.1

This model is an fine-tuned version of a Llama3 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner. The training process involved using the QLORA method.

## Model Details

- **Base Model**: Llama3 8B based LLM
- **Training Dataset**: High Quality Turkish instruction sets
- **Training Method**: SFT with QLORA

### QLORA Fine-Tuning Configuration

- `lora_alpha`: 128
- `lora_dropout`: 0
- `r`: 64
- `target_modules`: "q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj"
- `bias`: "none"

## Usage Examples

```python

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"KOCDIGITAL/Kocdigital-LLM-8b-v0.1", 
max_seq_length=4096)
model = AutoModelForCausalLM.from_pretrained(
    "KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
    load_in_4bit=True,
)

system = 'Sen Türkçe konuşan genel amaçlı bir asistansın. Her zaman kullanıcının verdiği talimatları doğru, kısa ve güzel bir gramer ile yerine getir.'

template = "{}\n\n###Talimat\n{}\n###Yanıt\n"
content = template.format(system, 'Türkiyenin 3 büyük ilini listeler misin.')

conv = []
conv.append({'role': 'user', 'content': content})
inputs = tokenizer.apply_chat_template(conv, 
                                       tokenize=False, 
                                       add_generation_prompt=True, 
                                       return_tensors="pt")

print(inputs)

inputs = tokenizer([inputs], 
                   return_tensors = "pt",
                   add_special_tokens=False).to("cuda")

outputs = model.generate(**inputs, 
                         max_new_tokens = 512, 
                         use_cache = True, 
                         do_sample = True, 
                         top_k = 50, 
                         top_p = 0.60, 
                         temperature = 0.3, 
                         repetition_penalty=1.1)

out_text = tokenizer.batch_decode(outputs)[0]
print(out_text)
```

# [Open LLM Turkish Leaderboard v0.2 Evaluation Results]
| Metric                          | Value |
|---------------------------------|------:|
| Avg.                            | 49.11 |
| AI2 Reasoning Challenge_tr-v0.2 | 44.03 |
| HellaSwag_tr-v0.2               | 46.73 |
| MMLU_tr-v0.2                    | 49.11 |
| TruthfulQA_tr-v0.2              | 48.51 |
| Winogrande _tr-v0.2             | 54.98 |
| GSM8k_tr-v0.2                   | 51.78 |