AdamLucek commited on
Commit
7edab5e
1 Parent(s): 0ba4db8

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +71 -35
README.md CHANGED
@@ -1,44 +1,80 @@
1
  ---
2
  base_model:
 
3
  - marketeam/Phi-Marketing
4
  - OEvortex/EMO-phi-128k
5
  library_name: transformers
6
  tags:
7
  - mergekit
8
  - merge
9
-
10
  ---
11
- # phi-3-market-emo
12
-
13
- This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
14
-
15
- ## Merge Details
16
- ### Merge Method
17
-
18
- This model was merged using the della merge method using [marketeam/Phi-Marketing](https://huggingface.co/marketeam/Phi-Marketing) as a base.
19
-
20
- ### Models Merged
21
-
22
- The following models were included in the merge:
23
- * [OEvortex/EMO-phi-128k](https://huggingface.co/OEvortex/EMO-phi-128k)
24
-
25
- ### Configuration
26
-
27
- The following YAML configuration was used to produce this model:
28
-
29
- ```yaml
30
- models:
31
- - model: marketeam/Phi-Marketing
32
- parameters:
33
- weight: 1.0
34
- - model: OEvortex/EMO-phi-128k
35
- parameters:
36
- weight: 1.0
37
- merge_method: della
38
- base_model: marketeam/Phi-Marketing
39
- parameters:
40
- density: 0.7
41
- lambda: 1.1
42
- epsilon: 0.2
43
-
44
- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  base_model:
3
+ - microsoft/Phi-3-mini-128k-instruct
4
  - marketeam/Phi-Marketing
5
  - OEvortex/EMO-phi-128k
6
  library_name: transformers
7
  tags:
8
  - mergekit
9
  - merge
10
+ license: apache-2.0
11
  ---
12
+ # Phi-3-mini-EmoMarketing-DELLA
13
+
14
+ This is a model based on [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) created by merging two fine-tuned versions together, one checkpoint for a domain-specific marketing fine tune, and one for emotional intelligence conversational setting.
15
+
16
+ ## 🤏 Models Merged
17
+
18
+ This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method using [marketeam/Phi-Marketing](https://huggingface.co/marketeam/Phi-Marketing) as a base.
19
+
20
+ The following models were included in the merge:
21
+ * [marketeam/Phi-Marketing](https://huggingface.co/marketeam/Phi-Marketing) <- Base
22
+ * [OEvortex/EMO-phi-128k](https://huggingface.co/OEvortex/EMO-phi-128k)
23
+
24
+ ## 🧩 Configuration
25
+
26
+ The following YAML configuration was used to produce this model:
27
+
28
+ ```yaml
29
+ models:
30
+ - model: marketeam/Phi-Marketing
31
+ parameters:
32
+ weight: 1.0
33
+ - model: OEvortex/EMO-phi-128k
34
+ parameters:
35
+ weight: 1.0
36
+ merge_method: della
37
+ base_model: marketeam/Phi-Marketing
38
+ parameters:
39
+ density: 0.7
40
+ lambda: 1.1
41
+ epsilon: 0.2
42
+
43
+ ```
44
+
45
+ ## 💻 Usage
46
+
47
+ ```python
48
+ from transformers import AutoTokenizer, AutoModelForCausalLM
49
+ import torch
50
+
51
+ # Load the tokenizer and model
52
+ tokenizer = AutoTokenizer.from_pretrained("AdamLucek/Phi-3-mini-EmoMarketing-DELLA", trust_remote_code=True)
53
+ model = AutoModelForCausalLM.from_pretrained(
54
+ "AdamLucek/Phi-3-mini-EmoMarketing-DELLA",
55
+ device_map="cuda",
56
+ torch_dtype=torch.bfloat16,
57
+ trust_remote_code=True
58
+ )
59
+
60
+ # Prepare the input text
61
+ input_text = "What are specific actionable ways to market products to technical software engineers with an emotional angle?"
62
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
63
+
64
+ # Generate the output
65
+ outputs = model.generate(
66
+ **input_ids,
67
+ max_new_tokens=256,
68
+ pad_token_id=tokenizer.eos_token_id
69
+ )
70
+
71
+ # Decode and print the generated text
72
+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
73
+ ```
74
+
75
+ **output**
76
+ >Hello there! 😊 I'd be happy to help you with that. When it comes to marketing products to technical software engineers with an emotional angle, there are several specific actionable ways to approach this. Here are a few ideas:
77
+ >1. Highlight the impact of the product on the user's personal and professional life. Emphasize how the product can solve a specific problem or improve the user's overall experience, and how it can positively impact their emotions and well-being.
78
+ >2. Use storytelling to create an emotional connection with the audience. Share real-life stories or testimonials from users who have experienced positive emotional outcomes as a result of using the product.
79
+ >3. Focus on the user's passions and interests. Understand what motivates and inspires technical software engineers, and tailor the marketing message to resonate with their emotional drivers.
80
+ >4. Use visual and sensory elements to evoke emotions. Incorporate imagery, colors, and sounds that align with the emotional tone you want to convey, and create a visually appealing and emotionally