Hugging Face's logo

HyperbeeAI
/
Tulpar-7b-v0

Text Generation
Transformers
PyTorch
English
llama
text-generation-inference
Inference Endpoints
Model card Files Community
2
Tulpar-7b-v0
File size: 4,120 Bytes 
d7c2bc5
 
 
 
 
 
 
 
3080190
 
 
 
 
 
 
 
 
aaa83be
 
 
 
 
 
 
 
 
 
 
 
d7c2bc5
aaa83be
 
 
 
 
d7c2bc5
aaa83be
3080190
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d7c2bc5
 
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
 
---
license: apache-2.0
language:
- en
library_name: transformers
thumbnail: "https://huggingface.co./HyperbeeAI/Tulpar-7b-v0/resolve/main/tulpar.png"
---

<p align="center">
  <img src="https://huggingface.co./HyperbeeAI/Tulpar-7b-v0/resolve/main/tulpar.png" width="360" height="360" >
</p>

# Model Description
Tulpar-7b is a LLama2-7b-based model trained by Hyperbee.ai. Training is done on a filtered and preprocessed instruction finetuning dataset that includes GPT-4 generated and generally curated datasets like Airoboros and Platypus. 

# Example Usage

Loading the model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HyperbeeAI/Tulpar-7b-v0")
model = AutoModelForCausalLM.from_pretrained("HyperbeeAI/Tulpar-7b-v0")
```

You can run inference with both of the following prompts:
```python
prompt = f"### User: {input_text}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=512)
```

```python
prompt = f"Question: {input_text}\n\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=512)
```


# Evaluation
Our offline HF Leaderboard evaluation results:
||||
|:------:|:--------:|:-------:|
|**Task**|**Metric**|**Value**|
|*arc_challenge*|acc_norm|0.5614|
|*hellaswag*|acc_norm|0.7901|
|*mmlu*|acc_norm|0.5242|
|*truthfulqa_mc*|mc2|0.5160|
|**Average**|-|**0.5979**||

Other GPT4All evaluation results:
||||
|:------:|:--------:|:-------:|
|**Task**|**Metric**|**Value**|
|boolq|acc   |0.8306|
|piqa|acc     |0.7905|
|    |acc_norm|0.7884|
|winogrande|acc   |0.7159|
|openbookqa|acc     |0.356|
|          |acc_norm|0.448|
|**Average** (including HF leaderboard datasets) | | 0.6468|

BigBenchHard results:
||||
|:------:|:--------:|:-------:|
|**Task**|**Metric**|**Value**|
|bigbench_causal_judgement                       |multiple_choice_grade|0.6105|
|bigbench_date_understanding                     |multiple_choice_grade|0.6423|
|bigbench_disambiguation_qa                      |multiple_choice_grade|0.3643|
|bigbench_dyck_languages                         |multiple_choice_grade|0.2000|
|bigbench_formal_fallacies_syllogisms_negation   |multiple_choice_grade|0.5002|
|bigbench_geometric_shapes                       |multiple_choice_grade|0.0000|
|                                                |exact_str_match      |0.0000|
|bigbench_hyperbaton                             |multiple_choice_grade|0.6754|
|bigbench_logical_deduction_five_objects         |multiple_choice_grade|0.2700|
|bigbench_logical_deduction_seven_objects        |multiple_choice_grade|0.1929|
|bigbench_logical_deduction_three_objects        |multiple_choice_grade|0.4133|
|bigbench_movie_recommendation                   |multiple_choice_grade|0.3000|
|bigbench_navigate                               |multiple_choice_grade|0.5000|
|bigbench_reasoning_about_colored_objects        |multiple_choice_grade|0.5750|
|bigbench_ruin_names                             |multiple_choice_grade|0.3281|
|bigbench_salient_translation_error_detection    |multiple_choice_grade|0.2976|
|bigbench_snarks                                 |multiple_choice_grade|0.6022|
|bigbench_sports_understanding                   |multiple_choice_grade|0.5122|
|bigbench_temporal_sequences                     |multiple_choice_grade|0.1450|
|bigbench_tracking_shuffled_objects_five_objects |multiple_choice_grade|0.1976|
|bigbench_tracking_shuffled_objects_seven_objects|multiple_choice_grade|0.1440|
|bigbench_tracking_shuffled_objects_three_objects|multiple_choice_grade|0.4133|
|**Average**| |0.3754

# Ethical Considerations and Limitations
Tulpar is a technology with potential risks and limitations. This model is finetuned only in English and all language-related scenarios are not covered. As Hyperbee.ai, we neither guarantee ethical, accurate, unbiased, objective responses nor endorse its outputs. Before deploying this model, you are advised to make safety tests for your use case.