Instructions to use LEAP-LAB-KUS/jevkt-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use LEAP-LAB-KUS/jevkt-checkpoints with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
pod backup 2026-11-07: e47/S1-E41-FRESH-A050-ts2-syn (0-11)
Browse files- e47/S1-E41-FRESH-A050-ts2-syn/DONE +1 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter/README.md +206 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter/adapter_config.json +57 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter/adapter_model.safetensors +3 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter_vllm/adapter_config.json +58 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter_vllm/adapter_model.safetensors +3 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter_vllm/decision_head.json +99 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/calibration.json +29 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/head.safetensors +3 -0
- e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/judge_config.json +164 -0
- e47/S1-E41-FRESH-A050-ts2-syn/train_log.jsonl +57 -0
e47/S1-E41-FRESH-A050-ts2-syn/DONE
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2026-11-07 12:24:33
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e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter/README.md
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| 1 |
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---
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| 2 |
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base_model: /workspace/jev_kt/models/hf/JEV-9B
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| 3 |
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library_name: peft
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| 4 |
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tags:
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| 5 |
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- base_model:adapter:/workspace/jev_kt/models/hf/JEV-9B
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- lora
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| 7 |
+
- transformers
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| 8 |
+
---
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| 9 |
+
|
| 10 |
+
# Model Card for Model ID
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| 11 |
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| 12 |
+
<!-- Provide a quick summary of what the model is/does. -->
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| 13 |
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| 14 |
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| 15 |
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| 16 |
+
## Model Details
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| 17 |
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| 18 |
+
### Model Description
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| 19 |
+
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| 20 |
+
<!-- Provide a longer summary of what this model is. -->
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| 21 |
+
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| 22 |
+
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| 23 |
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| 24 |
+
- **Developed by:** [More Information Needed]
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| 25 |
+
- **Funded by [optional]:** [More Information Needed]
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| 26 |
+
- **Shared by [optional]:** [More Information Needed]
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| 27 |
+
- **Model type:** [More Information Needed]
|
| 28 |
+
- **Language(s) (NLP):** [More Information Needed]
|
| 29 |
+
- **License:** [More Information Needed]
|
| 30 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
| 31 |
+
|
| 32 |
+
### Model Sources [optional]
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| 33 |
+
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| 34 |
+
<!-- Provide the basic links for the model. -->
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| 35 |
+
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| 36 |
+
- **Repository:** [More Information Needed]
|
| 37 |
+
- **Paper [optional]:** [More Information Needed]
|
| 38 |
+
- **Demo [optional]:** [More Information Needed]
|
| 39 |
+
|
| 40 |
+
## Uses
|
| 41 |
+
|
| 42 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
| 43 |
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|
| 44 |
+
### Direct Use
|
| 45 |
+
|
| 46 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
| 47 |
+
|
| 48 |
+
[More Information Needed]
|
| 49 |
+
|
| 50 |
+
### Downstream Use [optional]
|
| 51 |
+
|
| 52 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
| 53 |
+
|
| 54 |
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[More Information Needed]
|
| 55 |
+
|
| 56 |
+
### Out-of-Scope Use
|
| 57 |
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|
| 58 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 59 |
+
|
| 60 |
+
[More Information Needed]
|
| 61 |
+
|
| 62 |
+
## Bias, Risks, and Limitations
|
| 63 |
+
|
| 64 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
| 65 |
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|
| 66 |
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[More Information Needed]
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| 67 |
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|
| 68 |
+
### Recommendations
|
| 69 |
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| 70 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
| 71 |
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|
| 72 |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
| 73 |
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|
| 74 |
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## How to Get Started with the Model
|
| 75 |
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| 76 |
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Use the code below to get started with the model.
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| 77 |
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| 78 |
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[More Information Needed]
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| 79 |
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| 80 |
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## Training Details
|
| 81 |
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| 82 |
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### Training Data
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| 83 |
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| 84 |
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<!-- 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. -->
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| 85 |
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| 86 |
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[More Information Needed]
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| 87 |
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|
| 88 |
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### Training Procedure
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| 89 |
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| 90 |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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| 91 |
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| 92 |
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#### Preprocessing [optional]
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| 93 |
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| 94 |
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[More Information Needed]
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| 95 |
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|
| 96 |
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|
| 97 |
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#### Training Hyperparameters
|
| 98 |
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|
| 99 |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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| 100 |
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| 101 |
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#### Speeds, Sizes, Times [optional]
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| 102 |
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|
| 103 |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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| 104 |
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| 105 |
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[More Information Needed]
|
| 106 |
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|
| 107 |
+
## Evaluation
|
| 108 |
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| 109 |
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<!-- This section describes the evaluation protocols and provides the results. -->
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| 110 |
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| 111 |
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### Testing Data, Factors & Metrics
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| 112 |
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| 113 |
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#### Testing Data
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| 114 |
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| 115 |
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<!-- This should link to a Dataset Card if possible. -->
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| 116 |
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| 117 |
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[More Information Needed]
|
| 118 |
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| 119 |
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#### Factors
|
| 120 |
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| 121 |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
| 122 |
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|
| 123 |
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[More Information Needed]
|
| 124 |
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|
| 125 |
+
#### Metrics
|
| 126 |
+
|
| 127 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
| 128 |
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|
| 129 |
+
[More Information Needed]
|
| 130 |
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|
| 131 |
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### Results
|
| 132 |
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| 133 |
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[More Information Needed]
|
| 134 |
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|
| 135 |
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#### Summary
|
| 136 |
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|
| 137 |
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| 138 |
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| 139 |
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## Model Examination [optional]
|
| 140 |
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| 141 |
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<!-- Relevant interpretability work for the model goes here -->
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| 142 |
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| 143 |
+
[More Information Needed]
|
| 144 |
+
|
| 145 |
+
## Environmental Impact
|
| 146 |
+
|
| 147 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 148 |
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| 149 |
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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).
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| 150 |
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| 151 |
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- **Hardware Type:** [More Information Needed]
|
| 152 |
+
- **Hours used:** [More Information Needed]
|
| 153 |
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- **Cloud Provider:** [More Information Needed]
|
| 154 |
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- **Compute Region:** [More Information Needed]
|
| 155 |
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- **Carbon Emitted:** [More Information Needed]
|
| 156 |
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|
| 157 |
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## Technical Specifications [optional]
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| 158 |
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| 159 |
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### Model Architecture and Objective
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| 160 |
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| 161 |
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[More Information Needed]
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| 162 |
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| 163 |
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### Compute Infrastructure
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| 164 |
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| 165 |
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[More Information Needed]
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| 166 |
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| 167 |
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#### Hardware
|
| 168 |
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| 169 |
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[More Information Needed]
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| 170 |
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| 171 |
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#### Software
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| 172 |
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| 173 |
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[More Information Needed]
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| 174 |
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| 175 |
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## Citation [optional]
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| 176 |
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| 177 |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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| 178 |
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| 179 |
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**BibTeX:**
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| 180 |
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| 181 |
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[More Information Needed]
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| 182 |
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| 183 |
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**APA:**
|
| 184 |
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|
| 185 |
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[More Information Needed]
|
| 186 |
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|
| 187 |
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## Glossary [optional]
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| 188 |
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|
| 189 |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| 190 |
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[More Information Needed]
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| 192 |
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| 193 |
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## More Information [optional]
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| 194 |
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| 195 |
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[More Information Needed]
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| 196 |
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| 197 |
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## Model Card Authors [optional]
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| 198 |
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| 199 |
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[More Information Needed]
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| 200 |
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| 201 |
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## Model Card Contact
|
| 202 |
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| 203 |
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[More Information Needed]
|
| 204 |
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### Framework versions
|
| 205 |
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|
| 206 |
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- PEFT 0.21.2
|
e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter/adapter_config.json
ADDED
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@@ -0,0 +1,57 @@
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{
|
| 2 |
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"alora_invocation_tokens": null,
|
| 3 |
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"alpha_pattern": {},
|
| 4 |
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"arrow_config": null,
|
| 5 |
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"auto_mapping": {
|
| 6 |
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"base_model_class": "Qwen3_5ForCausalLM",
|
| 7 |
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"parent_library": "transformers.models.qwen3_5.modeling_qwen3_5"
|
| 8 |
+
},
|
| 9 |
+
"base_model_name_or_path": "/workspace/jev_kt/models/hf/JEV-9B",
|
| 10 |
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"bias": "none",
|
| 11 |
+
"corda_config": null,
|
| 12 |
+
"ensure_weight_tying": false,
|
| 13 |
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"eva_config": null,
|
| 14 |
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"exclude_modules": null,
|
| 15 |
+
"fan_in_fan_out": false,
|
| 16 |
+
"inference_mode": true,
|
| 17 |
+
"init_lora_weights": true,
|
| 18 |
+
"kasa_config": null,
|
| 19 |
+
"layer_replication": null,
|
| 20 |
+
"layers_pattern": null,
|
| 21 |
+
"layers_to_transform": null,
|
| 22 |
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"loftq_config": {},
|
| 23 |
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"lora_alpha": 32,
|
| 24 |
+
"lora_bias": false,
|
| 25 |
+
"lora_dropout": 0.05,
|
| 26 |
+
"lora_ga_config": null,
|
| 27 |
+
"megatron_config": null,
|
| 28 |
+
"megatron_core": "megatron.core",
|
| 29 |
+
"modules_to_save": null,
|
| 30 |
+
"monteclora_config": null,
|
| 31 |
+
"peft_type": "LORA",
|
| 32 |
+
"peft_version": "0.21.2",
|
| 33 |
+
"qalora_group_size": 16,
|
| 34 |
+
"r": 16,
|
| 35 |
+
"rank_pattern": {},
|
| 36 |
+
"revision": null,
|
| 37 |
+
"target_modules": [
|
| 38 |
+
"up_proj",
|
| 39 |
+
"q_proj",
|
| 40 |
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"in_proj_qkv",
|
| 41 |
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"gate_proj",
|
| 42 |
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"o_proj",
|
| 43 |
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"v_proj",
|
| 44 |
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"in_proj_z",
|
| 45 |
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"k_proj",
|
| 46 |
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"down_proj",
|
| 47 |
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"out_proj"
|
| 48 |
+
],
|
| 49 |
+
"target_parameters": null,
|
| 50 |
+
"task_type": null,
|
| 51 |
+
"trainable_token_indices": null,
|
| 52 |
+
"use_bdlora": null,
|
| 53 |
+
"use_dora": false,
|
| 54 |
+
"use_qalora": false,
|
| 55 |
+
"use_rslora": false,
|
| 56 |
+
"velora_config": null
|
| 57 |
+
}
|
e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter/adapter_model.safetensors
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:6c2a37f6c75db1a750f2ae5cb4fd5bf4d1355f96aeab1e5b6c29f72fd1ab4d6f
|
| 3 |
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size 160486456
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e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter_vllm/adapter_config.json
ADDED
|
@@ -0,0 +1,58 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
+
"base_model_class": "Qwen3_5ForCausalLM",
|
| 7 |
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"parent_library": "transformers.models.qwen3_5.modeling_qwen3_5"
|
| 8 |
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},
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| 9 |
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| 10 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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"fan_in_fan_out": false,
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| 16 |
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"inference_mode": true,
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| 17 |
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"init_lora_weights": true,
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| 18 |
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| 19 |
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| 24 |
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| 25 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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"monteclora_config": null,
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| 31 |
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"peft_type": "LORA",
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| 32 |
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"peft_version": "0.21.2",
|
| 33 |
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"qalora_group_size": 16,
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| 34 |
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"r": 32,
|
| 35 |
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"rank_pattern": {},
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| 36 |
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"revision": null,
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| 37 |
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"target_modules": [
|
| 38 |
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"down_proj",
|
| 39 |
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"gate_proj",
|
| 40 |
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"in_proj_qkv",
|
| 41 |
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"in_proj_z",
|
| 42 |
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"k_proj",
|
| 43 |
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"lm_head",
|
| 44 |
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"o_proj",
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| 45 |
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"out_proj",
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| 46 |
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"q_proj",
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| 47 |
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"up_proj",
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| 48 |
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"v_proj"
|
| 49 |
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],
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| 50 |
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"target_parameters": null,
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| 51 |
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"task_type": null,
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| 52 |
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| 53 |
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| 54 |
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"use_dora": false,
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| 55 |
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| 56 |
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| 57 |
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"velora_config": null
|
| 58 |
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}
|
e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter_vllm/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:16fb1e2a81f895c6f5e572528dae226aabe9ef2c67b2d2e5bd6fe99bf0ad9308
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| 3 |
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size 176641832
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e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/adapter_vllm/decision_head.json
ADDED
|
@@ -0,0 +1,99 @@
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| 99 |
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}
|
e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/calibration.json
ADDED
|
@@ -0,0 +1,29 @@
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{
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| 28 |
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"source": "models/e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000"
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| 29 |
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}
|
e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/head.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:ec9572e5fb2be36fae6178e20ad8fbd66cb386620384b60bd62f47303bc2c15e
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| 3 |
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size 393464
|
e47/S1-E41-FRESH-A050-ts2-syn/ckpt-16000/judge_config.json
ADDED
|
@@ -0,0 +1,164 @@
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|
| 1 |
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{
|
| 2 |
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"base_model_path": "/root/models/Qwen3.5-9B",
|
| 3 |
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| 4 |
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| 5 |
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| 7 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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"choice": [
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| 16 |
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| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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| 24 |
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|
| 25 |
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| 26 |
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| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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| 40 |
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|
e47/S1-E41-FRESH-A050-ts2-syn/train_log.jsonl
ADDED
|
@@ -0,0 +1,57 @@
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|
| 1 |
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{"time": "10:22:51", "event": "start", "data": "data/e18/G-d512-n16k", "data_manifest": {"arm": "G-d512-n16k", "name": null, "train_sha256": "ef94315960694ce58bdc6d61b8665b7bf46f4e2996b9e82503e1d17bb87a14b7", "n_train": 16000}, "init": "fresh", "objective": "s-log", "sigma": 0.5, "K": 8, "reward": "brier", "clip": 0.2, "passes": 1, "lr": 0.0001, "lr_head": 0.0002, "rows_per_step": 32, "train_seed": 2, "replay": "data/e16/replay/train.jsonl", "replay_frac": 0.05, "replay_sha256": "9dd2df19851678750cf79d2fdb47581720930ef2f0c6e18e6fbe23ef2181847e", "max_len": 3072, "schedule": "warmup 0.03 + cosine to x0.05 over 500 steps", "total_rows": 16000, "lora": {"r": 16, "lora_alpha": 32, "lora_dropout": 0.05}, "trainer_sha256": "a0b9d7552588ffb068565686509162a4f122f6f03392e693fa20088493e2a4f3", "device": "cuda", "load_s": 5.088741779327393}
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