File size: 5,417 Bytes
f7ab047
 
 
0b7d891
f7ab047
 
 
 
 
 
 
85649a3
f7ab047
 
76a2a00
f7ab047
 
 
 
 
0b7d891
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
 
0b7d891
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
 
0b7d891
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
 
0b7d891
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
 
0b7d891
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
 
0b7d891
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
 
0b7d891
f7ab047
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e9a13c6
 
 
f7ab047
c5f3846
 
f7ab047
c5f3846
 
 
 
 
 
 
 
 
f7ab047
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76a2a00
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
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
---
language:
- en
license: mit
library_name: transformers
tags:
- chat
- qwen
- qwen2
- finetune
- chatml
base_model: MaziyarPanahi/calme-2.1-rys-78b
datasets:
- MaziyarPanahi/truthy-dpo-v0.1-axolotl
- Intel/orca_dpo_pairs
model_name: calme-2.4-rys-78b
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
model-index:
- name: calme-2.4-rys-78b
  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: 80.11
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.4-rys-78b
      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: 62.16
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.4-rys-78b
      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: 37.69
      name: exact match
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.4-rys-78b
      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: 20.36
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.4-rys-78b
      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: 34.57
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.4-rys-78b
      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: 66.69
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.4-rys-78b
      name: Open LLM Leaderboard
---

<img src="./calme-2.webp" alt="Calme-2 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

# MaziyarPanahi/calme-2.4-rys-78b

This model is a fine-tuned version of the `dnhkng/RYS-XLarge`, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.

## Use Cases

This model is suitable for a wide range of applications, including but not limited to:

- Advanced question-answering systems
- Intelligent chatbots and virtual assistants
- Content generation and summarization
- Code generation and analysis
- Complex problem-solving and decision support

# ⚡ Quantized GGUF

Here are GGUF models thanks to @mradermacher:
- https://huggingface.co/mradermacher/calme-2.4-rys-78b-GGUF
- https://huggingface.co/mradermacher/calme-2.4-rys-78b-i1-GGUF

# 🏆 [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_MaziyarPanahi__calme-2.4-rys-78b)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |50.26|
|IFEval (0-Shot)    |80.11|
|BBH (3-Shot)       |62.16|
|MATH Lvl 5 (4-Shot)|37.69|
|GPQA (0-shot)      |20.36|
|MuSR (0-shot)      |34.57|
|MMLU-PRO (5-shot)  |66.69|


# Prompt Template

This model uses `ChatML` prompt template:

```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````

# How to use


```python

# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.4-rys-78b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.4-rys-78b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.4-rys-78b")
```


# Ethical Considerations

As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.