End of training

README.md

@@ -1,202 +1,210 @@
 ---
 base_model: EleutherAI/pythia-160m-deduped
 library_name: peft
+license: apache-2.0
+tags:
+- axolotl
+- relora
+- generated_from_trainer
+model-index:
+- name: pythia-160m-dolphin-extended
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
 
+axolotl version: `0.4.1`
+```yaml
+base_model: EleutherAI/pythia-160m-deduped
+load_in_8bit: 
+datasets:
+  - path: vicgalle/alpaca-gpt4
+    type: alpaca
+  - path: llamafactory/alpaca_gpt4_en
+    type: alpaca
+  - path: cognitivecomputations/dolphin
+    name: flan1m-alpaca-uncensored
+    type: alpaca
+    shards: 10
+
+dataset_prepared_path: ds-mega-alpaca
+#dataset_shard_num: 10
+chat_template: inst
+val_set_size: 0.001
+adapter: lora
+lora_model_dir: 
+sequence_len: 2048
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+  - query_key_value
+lora_target_linear: 
+lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
+lora_modules_to_save:
+  - embed_in
+  - embed_out
+  - lm_head
+lora_on_cpu: false
+# ReLoRA configuration
+# # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
+# relora_steps: # Number of steps per ReLoRA restart
+# relora_warmup_steps: # Number of per-restart warmup steps
+# relora_anneal_steps: # Number of anneal steps for each relora cycle
+# relora_prune_ratio: # threshold for optimizer magnitude when pruning
+# relora_cpu_offload:  # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
+relora_steps: 600
+relora_warmup_steps: 10
+relora_cpu_offload: true 
+wandb_project: pythia
+wandb_entity:
+wandb_watch:
+wandb_name: pythia-160m-dolphin-extended
+wandb_log_model:
+output_dir: ./outputs/lora-alpaca-pythia-160m-dolphin-extended
+gradient_accumulation_steps: 16
+micro_batch_size: 1
+num_epochs: 1
+learning_rate: 0.0006
+lr_scheduler: cosine_with_restarts
+#cosine_min_lr_ratio: 0.1
+train_on_inputs: false
+group_by_length: false
+#bf16: auto
+#fp16: true
+#tf32: false
+float16: true
+flash_attn: 
+xformers_attention: true
+optimizer: paged_adamw_8bit
+gpu_memory_limit: 8GiB
+hub_model_id: jtatman/pythia-160m-dolphin-extended
+early_stopping_patience: 10
+#resume_from_checkpoint: outputs/lora-alpaca-pythia-125m/checkpoint-51040
+auto_resume_from_checkpoints: true
+local_rank:
+weight_decay: 0.0
+#evals_per_epoch: 4
+eval_steps: 200
+logging_steps: 1
+save_steps: 200
+save_total_limit: 5
+warmup_steps: 100
+tokens:
+  - "[INST]"
+  - "[/INST]"
+
+```
+
+</details><br>
+
+# pythia-160m-dolphin-extended
+
+This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 5.3345
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0006
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine_with_restarts
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss |
+|:-------------:|:------:|:-----:|:---------------:|
+| 25.9906       | 0.0001 | 1     | 29.5451         |
+| 30.6876       | 0.0167 | 200   | 26.6061         |
+| 15.1401       | 0.0334 | 400   | 13.0583         |
+| 12.521        | 0.0500 | 600   | 10.7947         |
+| 10.212        | 0.0667 | 800   | 10.5847         |
+| 9.619         | 0.0834 | 1000  | 10.7486         |
+| 11.9315       | 0.1001 | 1200  | 10.9554         |
+| 14.3105       | 0.1167 | 1400  | 10.3818         |
+| 10.5925       | 0.1334 | 1600  | 10.6131         |
+| 8.7233        | 0.1501 | 1800  | 10.2776         |
+| 10.2267       | 0.1668 | 2000  | 10.0918         |
+| 12.8447       | 0.1835 | 2200  | 10.3923         |
+| 6.329         | 0.2001 | 2400  | 9.7525          |
+| 11.7827       | 0.2168 | 2600  | 10.3966         |
+| 13.6659       | 0.2335 | 2800  | 10.3891         |
+| 13.903        | 0.2502 | 3000  | 9.6615          |
+| 7.8718        | 0.2668 | 3200  | 9.7266          |
+| 11.3558       | 0.2835 | 3400  | 9.2946          |
+| 7.1755        | 0.3002 | 3600  | 8.7202          |
+| 8.2074        | 0.3169 | 3800  | 8.5147          |
+| 7.0288        | 0.3335 | 4000  | 7.2318          |
+| 9.7612        | 0.3502 | 4200  | 7.5585          |
+| 4.6886        | 0.3669 | 4400  | 7.0378          |
+| 11.0692       | 0.3836 | 4600  | 6.6091          |
+| 4.8223        | 0.4003 | 4800  | 6.7305          |
+| 6.6341        | 0.4169 | 5000  | 6.5858          |
+| 11.4613       | 0.4336 | 5200  | 6.5236          |
+| 12.5182       | 0.4503 | 5400  | 6.4048          |
+| 11.9191       | 0.4670 | 5600  | 6.4032          |
+| 7.9905        | 0.4836 | 5800  | 5.7290          |
+| 10.2991       | 0.5003 | 6000  | 5.7079          |
+| 4.6978        | 0.5170 | 6200  | 6.0383          |
+| 5.5322        | 0.5337 | 6400  | 5.8702          |
+| 8.5077        | 0.5504 | 6600  | 5.6017          |
+| 5.5676        | 0.5670 | 6800  | 5.8460          |
+| 5.0347        | 0.5837 | 7000  | 5.7875          |
+| 5.3157        | 0.6004 | 7200  | 5.4782          |
+| 6.8562        | 0.6171 | 7400  | 5.7030          |
+| 5.2433        | 0.6337 | 7600  | 5.5765          |
+| 4.4054        | 0.6504 | 7800  | 5.6948          |
+| 6.4413        | 0.6671 | 8000  | 5.4767          |
+| 4.5828        | 0.6838 | 8200  | 5.6491          |
+| 4.4912        | 0.7004 | 8400  | 5.7442          |
+| 5.2625        | 0.7171 | 8600  | 5.5131          |
+| 5.0451        | 0.7338 | 8800  | 5.6446          |
+| 4.7825        | 0.7505 | 9000  | 5.5226          |
+| 4.7226        | 0.7672 | 9200  | 5.4118          |
+| 6.0616        | 0.7838 | 9400  | 5.2987          |
+| 5.4928        | 0.8005 | 9600  | 5.2385          |
+| 6.1017        | 0.8172 | 9800  | 5.4942          |
+| 5.1683        | 0.8339 | 10000 | 5.2841          |
+| 4.4583        | 0.8505 | 10200 | 5.4625          |
+| 5.1028        | 0.8672 | 10400 | 5.4928          |
+| 4.4848        | 0.8839 | 10600 | 5.3151          |
+| 4.9981        | 0.9006 | 10800 | 5.3956          |
+| 4.7987        | 0.9173 | 11000 | 5.2824          |
+| 4.5008        | 0.9339 | 11200 | 5.6660          |
+| 4.037         | 0.9506 | 11400 | 5.6325          |
+| 4.5158        | 0.9673 | 11600 | 5.3345          |
 
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-
-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
-
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-
-## 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]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-
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-
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-### Recommendations
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-<!-- 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
-
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-
-#### 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
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-#### Factors
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
-
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
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-## 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]
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-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-## Citation [optional]
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 ### Framework versions
 
-- PEFT 0.11.1
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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config.json

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   "rotary_pct": 0.25,
   "tie_word_embeddings": false,
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   "use_cache": false,
   "use_parallel_residual": true,

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