Model save

README.md

@@ -1,209 +1,68 @@
 ---
-language: en
-model-index:
-- name: obiwit/llama3.2-3b-dpo-mini
-  results:
-  - task:
-      type: preference_evaluation
-    dataset:
-      name: criteria-faireval
-      type: obiwit/criteria-faireval
-    metrics:
-    - type: accuracy
-      value: 0.5402380952380952
+base_model: CriteriaPO/llama3.2-3b-sft
+library_name: transformers
+model_name: llama3.2-3b-dpo-mini
+tags:
+- generated_from_trainer
+- trl
+- dpo
+licence: license
 ---
 
-# Model Card for obiwit/llama3.2-3b-dpo-mini
+# Model Card for llama3.2-3b-dpo-mini
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a fine-tuned version of [CriteriaPO/llama3.2-3b-sft](https://huggingface.co/CriteriaPO/llama3.2-3b-sft).
+It has been trained using [TRL](https://github.com/huggingface/trl).
 
+## Quick start
 
+```python
+from transformers import pipeline
 
-## Model Details
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="obiwit/llama3.2-3b-dpo-mini", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
 
-### Model Description
+## Training procedure
 
-<!-- Provide a longer summary of what this model is. -->
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/reql6e82)
 
+This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
 
+### Framework versions
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- TRL: 0.12.2
+- Transformers: 4.46.3
+- Pytorch: 2.1.2+cu121
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
 
-### Model Sources [optional]
+## Citations
 
-<!-- Provide the basic links for the model. -->
+Cite DPO as:
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```bibtex
+@inproceedings{rafailov2023direct,
+    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
+    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
+    year         = 2023,
+    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
+    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
+    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
+}
+```
 
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

all_results.json

@@ -1,22 +1,9 @@
 {
     "epoch": 0.9999360981532366,
-    "eval_logits/chosen": -2.921875,
-    "eval_logits/rejected": -3.078125,
-    "eval_logps/chosen": -296.0,
-    "eval_logps/rejected": -576.0,
-    "eval_loss": 0.27326104044914246,
-    "eval_rewards/accuracies": 0.8810483813285828,
-    "eval_rewards/chosen": -1.71875,
-    "eval_rewards/margins": 2.96875,
-    "eval_rewards/rejected": -4.6875,
-    "eval_runtime": 1472.2304,
-    "eval_samples": 55582,
-    "eval_samples_per_second": 37.702,
-    "eval_steps_per_second": 0.59,
     "total_flos": 0.0,
-    "train_loss": 0.2751994705882784,
-    "train_runtime": 76988.6649,
+    "train_loss": 0.2718314818565587,
+    "train_runtime": 68698.2546,
     "train_samples": 1001516,
-    "train_samples_per_second": 13.009,
-    "train_steps_per_second": 0.102
+    "train_samples_per_second": 14.578,
+    "train_steps_per_second": 0.114
 }

train_results.json

@@ -1,9 +1,9 @@
 {
     "epoch": 0.9999360981532366,
     "total_flos": 0.0,
-    "train_loss": 0.2751994705882784,
-    "train_runtime": 76988.6649,
+    "train_loss": 0.2718314818565587,
+    "train_runtime": 68698.2546,
     "train_samples": 1001516,
-    "train_samples_per_second": 13.009,
-    "train_steps_per_second": 0.102
+    "train_samples_per_second": 14.578,
+    "train_steps_per_second": 0.114
 }