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
backup e16/S1-MIX3-n16k-syn (Pod B)
Browse files- e16/S1-MIX3-n16k-syn/ckpt-16000/adapter/README.md +206 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/adapter/adapter_config.json +57 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/adapter/adapter_model.safetensors +3 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/adapter_vllm/adapter_config.json +58 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/adapter_vllm/adapter_model.safetensors +3 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/adapter_vllm/decision_head.json +99 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/calibration.json +29 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/head.safetensors +3 -0
- e16/S1-MIX3-n16k-syn/ckpt-16000/judge_config.json +164 -0
- e16/S1-MIX3-n16k-syn/train_log.jsonl +57 -0
e16/S1-MIX3-n16k-syn/ckpt-16000/adapter/README.md
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| 1 |
+
---
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| 2 |
+
base_model: /workspace/jev_kt/models/hf/JEV-9B
|
| 3 |
+
library_name: peft
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| 4 |
+
tags:
|
| 5 |
+
- base_model:adapter:/workspace/jev_kt/models/hf/JEV-9B
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| 6 |
+
- lora
|
| 7 |
+
- transformers
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
# Model Card for Model ID
|
| 11 |
+
|
| 12 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
| 13 |
+
|
| 14 |
+
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| 15 |
+
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| 16 |
+
## Model Details
|
| 17 |
+
|
| 18 |
+
### Model Description
|
| 19 |
+
|
| 20 |
+
<!-- Provide a longer summary of what this model is. -->
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
- **Developed by:** [More Information Needed]
|
| 25 |
+
- **Funded by [optional]:** [More Information Needed]
|
| 26 |
+
- **Shared by [optional]:** [More Information Needed]
|
| 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]
|
| 33 |
+
|
| 34 |
+
<!-- Provide the basic links for the model. -->
|
| 35 |
+
|
| 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 |
+
|
| 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 |
+
[More Information Needed]
|
| 55 |
+
|
| 56 |
+
### Out-of-Scope Use
|
| 57 |
+
|
| 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 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
+
### Recommendations
|
| 69 |
+
|
| 70 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
| 71 |
+
|
| 72 |
+
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 |
+
|
| 74 |
+
## How to Get Started with the Model
|
| 75 |
+
|
| 76 |
+
Use the code below to get started with the model.
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
## Training Details
|
| 81 |
+
|
| 82 |
+
### Training Data
|
| 83 |
+
|
| 84 |
+
<!-- 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. -->
|
| 85 |
+
|
| 86 |
+
[More Information Needed]
|
| 87 |
+
|
| 88 |
+
### Training Procedure
|
| 89 |
+
|
| 90 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
| 91 |
+
|
| 92 |
+
#### Preprocessing [optional]
|
| 93 |
+
|
| 94 |
+
[More Information Needed]
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
#### Training Hyperparameters
|
| 98 |
+
|
| 99 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
| 100 |
+
|
| 101 |
+
#### Speeds, Sizes, Times [optional]
|
| 102 |
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|
| 103 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
| 104 |
+
|
| 105 |
+
[More Information Needed]
|
| 106 |
+
|
| 107 |
+
## Evaluation
|
| 108 |
+
|
| 109 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
| 110 |
+
|
| 111 |
+
### Testing Data, Factors & Metrics
|
| 112 |
+
|
| 113 |
+
#### Testing Data
|
| 114 |
+
|
| 115 |
+
<!-- This should link to a Dataset Card if possible. -->
|
| 116 |
+
|
| 117 |
+
[More Information Needed]
|
| 118 |
+
|
| 119 |
+
#### Factors
|
| 120 |
+
|
| 121 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
#### Metrics
|
| 126 |
+
|
| 127 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
| 128 |
+
|
| 129 |
+
[More Information Needed]
|
| 130 |
+
|
| 131 |
+
### Results
|
| 132 |
+
|
| 133 |
+
[More Information Needed]
|
| 134 |
+
|
| 135 |
+
#### Summary
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
## Model Examination [optional]
|
| 140 |
+
|
| 141 |
+
<!-- Relevant interpretability work for the model goes here -->
|
| 142 |
+
|
| 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 |
+
|
| 149 |
+
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).
|
| 150 |
+
|
| 151 |
+
- **Hardware Type:** [More Information Needed]
|
| 152 |
+
- **Hours used:** [More Information Needed]
|
| 153 |
+
- **Cloud Provider:** [More Information Needed]
|
| 154 |
+
- **Compute Region:** [More Information Needed]
|
| 155 |
+
- **Carbon Emitted:** [More Information Needed]
|
| 156 |
+
|
| 157 |
+
## Technical Specifications [optional]
|
| 158 |
+
|
| 159 |
+
### Model Architecture and Objective
|
| 160 |
+
|
| 161 |
+
[More Information Needed]
|
| 162 |
+
|
| 163 |
+
### Compute Infrastructure
|
| 164 |
+
|
| 165 |
+
[More Information Needed]
|
| 166 |
+
|
| 167 |
+
#### Hardware
|
| 168 |
+
|
| 169 |
+
[More Information Needed]
|
| 170 |
+
|
| 171 |
+
#### Software
|
| 172 |
+
|
| 173 |
+
[More Information Needed]
|
| 174 |
+
|
| 175 |
+
## Citation [optional]
|
| 176 |
+
|
| 177 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 178 |
+
|
| 179 |
+
**BibTeX:**
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
**APA:**
|
| 184 |
+
|
| 185 |
+
[More Information Needed]
|
| 186 |
+
|
| 187 |
+
## Glossary [optional]
|
| 188 |
+
|
| 189 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
| 190 |
+
|
| 191 |
+
[More Information Needed]
|
| 192 |
+
|
| 193 |
+
## More Information [optional]
|
| 194 |
+
|
| 195 |
+
[More Information Needed]
|
| 196 |
+
|
| 197 |
+
## Model Card Authors [optional]
|
| 198 |
+
|
| 199 |
+
[More Information Needed]
|
| 200 |
+
|
| 201 |
+
## Model Card Contact
|
| 202 |
+
|
| 203 |
+
[More Information Needed]
|
| 204 |
+
### Framework versions
|
| 205 |
+
|
| 206 |
+
- PEFT 0.21.2
|
e16/S1-MIX3-n16k-syn/ckpt-16000/adapter/adapter_config.json
ADDED
|
@@ -0,0 +1,57 @@
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| 1 |
+
{
|
| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": {
|
| 6 |
+
"base_model_class": "Qwen3_5ForCausalLM",
|
| 7 |
+
"parent_library": "transformers.models.qwen3_5.modeling_qwen3_5"
|
| 8 |
+
},
|
| 9 |
+
"base_model_name_or_path": "/workspace/jev_kt/models/hf/JEV-9B",
|
| 10 |
+
"bias": "none",
|
| 11 |
+
"corda_config": null,
|
| 12 |
+
"ensure_weight_tying": false,
|
| 13 |
+
"eva_config": null,
|
| 14 |
+
"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 |
+
"loftq_config": {},
|
| 23 |
+
"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": "0.21.0",
|
| 33 |
+
"qalora_group_size": 16,
|
| 34 |
+
"r": 16,
|
| 35 |
+
"rank_pattern": {},
|
| 36 |
+
"revision": null,
|
| 37 |
+
"target_modules": [
|
| 38 |
+
"q_proj",
|
| 39 |
+
"v_proj",
|
| 40 |
+
"up_proj",
|
| 41 |
+
"out_proj",
|
| 42 |
+
"in_proj_qkv",
|
| 43 |
+
"down_proj",
|
| 44 |
+
"o_proj",
|
| 45 |
+
"k_proj",
|
| 46 |
+
"gate_proj",
|
| 47 |
+
"in_proj_z"
|
| 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 |
+
}
|
e16/S1-MIX3-n16k-syn/ckpt-16000/adapter/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:76ac886d705e1a762c8a6d8a3bc28228499565caeb4d7ab38b0b3079d38c07c3
|
| 3 |
+
size 160486456
|
e16/S1-MIX3-n16k-syn/ckpt-16000/adapter_vllm/adapter_config.json
ADDED
|
@@ -0,0 +1,58 @@
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| 8 |
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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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"qalora_group_size": 16,
|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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"target_modules": [
|
| 38 |
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"down_proj",
|
| 39 |
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|
| 40 |
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"in_proj_qkv",
|
| 41 |
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"in_proj_z",
|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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],
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| 50 |
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|
| 51 |
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|
| 52 |
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| 53 |
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| 58 |
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|
e16/S1-MIX3-n16k-syn/ckpt-16000/adapter_vllm/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:e22b519ae133c24aa698913b35c3829c97ee20ca8552b9d18a8e39a0a05d694c
|
| 3 |
+
size 176641832
|
e16/S1-MIX3-n16k-syn/ckpt-16000/adapter_vllm/decision_head.json
ADDED
|
@@ -0,0 +1,99 @@
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| 29 |
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|
| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 38 |
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|
| 40 |
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| 75 |
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| 80 |
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| 85 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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| 98 |
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|
| 99 |
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|
e16/S1-MIX3-n16k-syn/ckpt-16000/calibration.json
ADDED
|
@@ -0,0 +1,29 @@
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|
| 1 |
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{
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| 28 |
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"source": "models/e16/S1-MIX3-n16k-syn/ckpt-16000"
|
| 29 |
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|
e16/S1-MIX3-n16k-syn/ckpt-16000/head.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:fe7605b7cee6b4aaf269d3f29e877a5545b5a0e71358173ec648c919619bf6bc
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| 3 |
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size 393464
|
e16/S1-MIX3-n16k-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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|
| 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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| 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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|
| 41 |
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| 42 |
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| 43 |
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| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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},
|
| 48 |
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| 49 |
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| 50 |
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| 54 |
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| 55 |
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|
| 56 |
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| 57 |
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|
| 58 |
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| 63 |
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| 64 |
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| 65 |
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| 67 |
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| 68 |
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| 69 |
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44,
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| 70 |
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| 71 |
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| 72 |
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47
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 82 |
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| 84 |
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| 85 |
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| 93 |
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| 96 |
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| 97 |
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| 98 |
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| 164 |
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|
e16/S1-MIX3-n16k-syn/train_log.jsonl
ADDED
|
@@ -0,0 +1,57 @@
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|
| 1 |
+
{"time": "08:31:03", "event": "start", "data": "data/e20/MIX3-n16k", "data_manifest": {"arm": "MIX3-n16k", "name": null, "train_sha256": "7c017c357f68f614b9b636ef6fe1670c25fcd274d3ba6e9240b5f36033e5bca4", "n_train": 16000}, "init": "released", "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": 0, "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": "bc7c08e836bd99bb2665b02903cd41404ea029ecfa9b68a9ec1a7cda96958ba4", "device": "cuda", "load_s": 80.05717611312866}
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| 7 |
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{"time": "10:48:13", "event": "step", "step": 390, "rows_seen": 12480, "lr": 1.6556141248659472e-05, "tok_per_s": 851.0403489633683, "loss": 0.5868439711630344, "entropy": 0.5793858133256435, "sharp": 0.19167248532176018}
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{"time": "10:51:32", "event": "step", "step": 400, "rows_seen": 12800, "lr": 1.4621466498270773e-05, "tok_per_s": 851.8356387517538, "loss": 0.5724565386772156, "entropy": 0.5595830082893372, "sharp": 0.209070585668087}
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| 45 |
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{"time": "10:52:55", "event": "val", "step": 400, "rows_seen": 12800, "n": 500, "dropped_long": 0, "noul_ce": 0.5719676613807678, "noul_T": 0.8597087521762673, "noul_auc": 0.755734080437461, "noul_acc": 0.714, "noul_f1": 0.7782945736434108, "noul_nll": 0.5692778851464391, "noul_ece": 0.06344858980178833, "noul_brier": 0.19344334194989585, "noul_sharp": 0.17959153652191162, "entropy": 0.5950776934623718}
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{"time": "10:56:16", "event": "step", "step": 410, "rows_seen": 13120, "lr": 1.284566748110963e-05, "tok_per_s": 844.161377589308, "loss": 0.5621709935367107, "entropy": 0.5608640089631081, "sharp": 0.21343966759741306}
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{"time": "10:59:44", "event": "step", "step": 420, "rows_seen": 13440, "lr": 1.1236192515387821e-05, "tok_per_s": 844.835269626916, "loss": 0.5919195041060448, "entropy": 0.5926895067095757, "sharp": 0.18045827373862267}
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{"time": "11:03:14", "event": "step", "step": 430, "rows_seen": 13760, "lr": 9.799792298443728e-06, "tok_per_s": 845.629866666841, "loss": 0.6319852359592915, "entropy": 0.6180271878838539, "sharp": 0.15365645661950111}
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| 49 |
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{"time": "11:06:39", "event": "step", "step": 440, "rows_seen": 14080, "lr": 8.542491591971554e-06, "tok_per_s": 846.2405706370508, "loss": 0.6109349951148033, "entropy": 0.5499566905200481, "sharp": 0.22229031287133694}
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| 50 |
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{"time": "11:10:10", "event": "step", "step": 450, "rows_seen": 14400, "lr": 7.469563952077155e-06, "tok_per_s": 846.9379508454083, "loss": 0.5560286678373814, "entropy": 0.5911026783287525, "sharp": 0.18553075194358826}
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| 51 |
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{"time": "11:13:31", "event": "step", "step": 460, "rows_seen": 14720, "lr": 6.585509610150972e-06, "tok_per_s": 847.5046881410133, "loss": 0.6414517425000668, "entropy": 0.5996933449059725, "sharp": 0.18129407241940498}
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| 52 |
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{"time": "11:16:52", "event": "step", "step": 470, "rows_seen": 15040, "lr": 5.894036597333646e-06, "tok_per_s": 848.1044766206044, "loss": 0.6224712021648884, "entropy": 0.6070730686187744, "sharp": 0.17572548799216747}
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| 53 |
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{"time": "11:20:11", "event": "step", "step": 480, "rows_seen": 15360, "lr": 5.398045191744809e-06, "tok_per_s": 848.7760633961865, "loss": 0.6015110500156879, "entropy": 0.5780304186046124, "sharp": 0.20515719428658485}
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| 55 |
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| 56 |
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