manny-uncharted commited on
Commit
14ba759
·
verified ·
1 Parent(s): b1e1a40

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +81 -166
README.md CHANGED
@@ -1,202 +1,117 @@
1
  ---
2
  base_model: EleutherAI/pythia-70m-deduped
 
3
  library_name: peft
 
 
 
 
 
 
 
 
 
 
 
4
  ---
5
 
6
- # Model Card for Model ID
7
-
8
- <!-- Provide a quick summary of what the model is/does. -->
9
-
10
 
 
11
 
12
  ## Model Details
13
 
14
  ### Model Description
15
 
16
- <!-- Provide a longer summary of what this model is. -->
17
-
18
 
19
-
20
- - **Developed by:** [More Information Needed]
21
- - **Funded by [optional]:** [More Information Needed]
22
- - **Shared by [optional]:** [More Information Needed]
23
- - **Model type:** [More Information Needed]
24
- - **Language(s) (NLP):** [More Information Needed]
25
- - **License:** [More Information Needed]
26
- - **Finetuned from model [optional]:** [More Information Needed]
27
 
28
  ### Model Sources [optional]
29
 
30
- <!-- Provide the basic links for the model. -->
31
-
32
- - **Repository:** [More Information Needed]
33
- - **Paper [optional]:** [More Information Needed]
34
- - **Demo [optional]:** [More Information Needed]
35
 
36
  ## Uses
37
 
38
- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
-
40
  ### Direct Use
41
 
42
- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
43
 
44
- [More Information Needed]
45
 
46
  ### Downstream Use [optional]
47
 
48
- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
-
50
- [More Information Needed]
51
 
52
  ### Out-of-Scope Use
53
 
54
- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
-
56
- [More Information Needed]
 
57
 
58
  ## Bias, Risks, and Limitations
59
 
60
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
-
62
- [More Information Needed]
 
 
63
 
64
  ### Recommendations
65
 
66
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
-
68
- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
69
 
70
  ## How to Get Started with the Model
71
 
72
- Use the code below to get started with the model.
73
-
74
- [More Information Needed]
75
-
76
- ## Training Details
77
-
78
- ### Training Data
79
-
80
- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
-
82
- [More Information Needed]
83
-
84
- ### Training Procedure
85
-
86
- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
-
88
- #### Preprocessing [optional]
89
-
90
- [More Information Needed]
91
-
92
-
93
- #### Training Hyperparameters
94
-
95
- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
-
97
- #### Speeds, Sizes, Times [optional]
98
-
99
- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
-
101
- [More Information Needed]
102
-
103
- ## Evaluation
104
-
105
- <!-- This section describes the evaluation protocols and provides the results. -->
106
-
107
- ### Testing Data, Factors & Metrics
108
-
109
- #### Testing Data
110
-
111
- <!-- This should link to a Dataset Card if possible. -->
112
-
113
- [More Information Needed]
114
-
115
- #### Factors
116
-
117
- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
-
119
- [More Information Needed]
120
-
121
- #### Metrics
122
-
123
- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
-
125
- [More Information Needed]
126
-
127
- ### Results
128
-
129
- [More Information Needed]
130
-
131
- #### Summary
132
-
133
-
134
-
135
- ## Model Examination [optional]
136
-
137
- <!-- Relevant interpretability work for the model goes here -->
138
-
139
- [More Information Needed]
140
-
141
- ## Environmental Impact
142
-
143
- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
-
145
- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
-
147
- - **Hardware Type:** [More Information Needed]
148
- - **Hours used:** [More Information Needed]
149
- - **Cloud Provider:** [More Information Needed]
150
- - **Compute Region:** [More Information Needed]
151
- - **Carbon Emitted:** [More Information Needed]
152
-
153
- ## Technical Specifications [optional]
154
-
155
- ### Model Architecture and Objective
156
-
157
- [More Information Needed]
158
-
159
- ### Compute Infrastructure
160
-
161
- [More Information Needed]
162
-
163
- #### Hardware
164
-
165
- [More Information Needed]
166
-
167
- #### Software
168
-
169
- [More Information Needed]
170
-
171
- ## Citation [optional]
172
-
173
- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
-
175
- **BibTeX:**
176
-
177
- [More Information Needed]
178
-
179
- **APA:**
180
-
181
- [More Information Needed]
182
-
183
- ## Glossary [optional]
184
-
185
- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
-
187
- [More Information Needed]
188
-
189
- ## More Information [optional]
190
-
191
- [More Information Needed]
192
-
193
- ## Model Card Authors [optional]
194
-
195
- [More Information Needed]
196
-
197
- ## Model Card Contact
198
-
199
- [More Information Needed]
200
- ### Framework versions
201
-
202
- - PEFT 0.7.1
 
1
  ---
2
  base_model: EleutherAI/pythia-70m-deduped
3
+ model_name: "Pythia-70M Sarcasm LoRA by hyvve"
4
  library_name: peft
5
+ tags:
6
+ - text-generation
7
+ - lora
8
+ - peft
9
+ - sarcasm
10
+ - pythia
11
+ - fine-tuning
12
+ - causal-lm
13
+ - EleutherAI
14
+ license: apache-2.0
15
+ pipeline_tag: text-generation
16
  ---
17
 
18
+ # Model Card for Pythia-70M Sarcasm LoRA
 
 
 
19
 
20
+ This model is a LoRA (Low-Rank Adaptation) fine-tune of the `EleutherAI/pythia-70m-deduped` model, specifically adapted for tasks related to sarcasm.
21
 
22
  ## Model Details
23
 
24
  ### Model Description
25
 
26
+ This is a PEFT LoRA adapter for the `EleutherAI/pythia-70m-deduped` model. It has been fine-tuned on a dataset related to sarcasm. As a Causal Language Model (CLM), its primary function is to predict the next token in a sequence. This fine-tuning aims to imbue the model with an understanding or stylistic representation of sarcastic language.
 
27
 
28
+ - **Developed by:** [hyvve](https://hyvve.xyz) (based on job configurations)
29
+ - **Model type:** Causal Language Model (specifically, a LoRA adapter for a GPT-NeoX based model)
30
+ - **Language(s) (NLP):** English (derived from the base model and assumed dataset language)
31
+ - **License:** Apache-2.0 (inherited from the base model `EleutherAI/pythia-70m-deduped`)
32
+ - **Finetuned from model:** `EleutherAI/pythia-70m-deduped`
 
 
 
33
 
34
  ### Model Sources [optional]
35
 
36
+ - **Repository (LoRA Adapter):** `https://huggingface.co/manny-uncharted/pythia-70m-sarcasm-lora` (based on `hf_target_model_repo_id`)
37
+ - **Base Model Repository:** `https://huggingface.co/EleutherAI/pythia-70m-deduped`
38
+ - **Paper [optional]:** For Pythia suite: [arXiv:2304.01373](https://arxiv.org/abs/2304.01373)
39
+ - **Demo [optional]:** [Not Provided]
 
40
 
41
  ## Uses
42
 
 
 
43
  ### Direct Use
44
 
45
+ This LoRA adapter is intended to be loaded on top of the `EleutherAI/pythia-70m-deduped` base model. It can be used for:
46
+ * Generating text with a sarcastic tone or style.
47
+ * Completing prompts in a sarcastic manner.
48
+ * Research into modeling nuanced aspects of language like sarcasm with smaller LMs.
49
 
50
+ **Note:** Due to the extremely small dataset size used for fine-tuning (14 examples), the model's ability to robustly generate or understand sarcasm will be very limited. It primarily serves as a pipeline and integration test.
51
 
52
  ### Downstream Use [optional]
53
 
54
+ * Further fine-tuning on larger, more diverse sarcasm datasets.
55
+ * Integration into applications requiring conditional text generation with a sarcastic flavor (e.g., chatbots, creative writing tools), though extensive further tuning would be necessary.
 
56
 
57
  ### Out-of-Scope Use
58
 
59
+ * Reliable sarcasm detection or classification without significant further development and evaluation.
60
+ * Generating harmful, biased, or offensive content, even if framed as sarcasm.
61
+ * Use in critical applications where misinterpretation of sarcasm could have negative consequences.
62
+ * Generating fluent, coherent, and factually accurate long-form text beyond the capabilities of the 70M parameter base model.
63
 
64
  ## Bias, Risks, and Limitations
65
 
66
+ * **Limited Scope:** Fine-tuned on a very small dataset (1000 examples), so its understanding and generation of sarcasm will be superficial and not generalizable.
67
+ * **Inherited Biases:** Inherits biases from the `EleutherAI/pythia-70m-deduped` base model, which was trained on The Pile. These can include societal, gender, and racial biases.
68
+ * **Misinterpretation of Sarcasm:** Sarcasm is highly context-dependent and subjective. The model may generate text that is inappropriately sarcastic or fail to understand sarcastic prompts correctly.
69
+ * **Potential for Harmful Sarcasm:** Sarcasm can be used to convey negativity or veiled aggression. The model might inadvertently generate such content.
70
+ * **Numerical Instability:** During the logged training run, an `eval_loss: nan` was observed, indicating potential issues with evaluation on the tiny validation set or numerical instability under the given configuration. The `train_loss: 0.0` also suggests extreme overfitting or issues with the learning process on such limited data.
71
 
72
  ### Recommendations
73
 
74
+ * **Thorough Evaluation:** Before any production use, the model (after further fine-tuning on a substantial dataset) would require rigorous evaluation for both sarcasm generation quality and potential biases.
75
+ * **Content Moderation:** Downstream applications should implement content moderation and safety filters.
76
+ * **Context is Key:** Use with clear context and be aware that its sarcastic capabilities are likely very brittle due to the limited training data.
77
+ * **Do Not Use for Critical Decisions:** This model, in its current state, is not suitable for any critical applications.
78
 
79
  ## How to Get Started with the Model
80
 
81
+ To use this LoRA adapter, you'll need to load the base model and then apply the adapter using the PEFT library.
82
+
83
+ ```python
84
+ from transformers import AutoModelForCausalLM, AutoTokenizer
85
+ from peft import PeftModel
86
+ import torch
87
+
88
+ base_model_id = "EleutherAI/pythia-70m-deduped"
89
+ adapter_model_id = "manny-uncharted/pythia-70m-sarcasm-lora" # Replace with your actual model ID
90
+
91
+ # Load the tokenizer
92
+ tokenizer = AutoTokenizer.from_pretrained(base_model_id)
93
+ if tokenizer.pad_token is None:
94
+ tokenizer.pad_token = tokenizer.eos_token
95
+
96
+ # Load the base model (e.g., in 4-bit if that's how the adapter was trained/intended)
97
+ # For QLoRA, BitsAndBytesConfig would be needed here as during training
98
+ # For simplicity, this example loads without quantization. Adapt as needed.
99
+ base_model = AutoModelForCausalLM.from_pretrained(
100
+ base_model_id,
101
+ # quantization_config=BitsAndBytesConfig(...) # Add if loading in 4-bit/8-bit
102
+ # torch_dtype=torch.float16, # Or torch.bfloat16
103
+ device_map="auto"
104
+ )
105
+
106
+ # Load the PEFT LoRA model (adapter)
107
+ model = PeftModel.from_pretrained(base_model, adapter_model_id)
108
+ model = model.merge_and_unload() # Optional: merge adapter into base model for faster inference
109
+
110
+ # Now you can use the model for generation
111
+ prompt = "The weather today is just " # Example prompt
112
+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
113
+
114
+ # Generate text
115
+ # Adjust generation parameters as needed
116
+ outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)
117
+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))