End of training

README.md

@@ -1,202 +1,162 @@
 ---
 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: lee-ite/med-alpaca
+    type: alpaca
+    shards: 4
+  - path: vicgalle/alpaca-gpt4
+    type: alpaca
+  - path: iamtarun/python_code_instructions_18k_alpaca
+    type: alpaca
+  - path: llamafactory/alpaca_gpt4_en
+    type: alpaca
+  - path: cognitivecomputations/dolphin
+    name: flan1m-alpaca-uncensored
+    type: alpaca
+    shards: 4
+
+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: 32
+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: 200
+relora_warmup_steps: 10
+relora_cpu_offload: false
+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: 3
+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: 3
+#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: 9.6289
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 38.0524       | 0.0000 | 1    | 33.0385         |
+| 8.859         | 0.0056 | 200  | 8.2423          |
+| 7.2059        | 0.0113 | 400  | 7.4385          |
+| 10.5864       | 0.0169 | 600  | 10.5324         |
+| 10.3914       | 0.0226 | 800  | 10.2817         |
+| 9.5214        | 0.0282 | 1000 | 9.6289          |
 
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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]
 ### 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

adapter_model.safetensors

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config.json

@@ -22,7 +22,7 @@
   "rotary_emb_base": 10000,
   "rotary_pct": 0.25,
   "tie_word_embeddings": false,
-  "torch_dtype": "float16",
+  "torch_dtype": "bfloat16",
   "transformers_version": "4.41.2",
   "use_cache": false,
   "use_parallel_residual": true,

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": 0,
+  "transformers_version": "4.41.2"
+}

pytorch_model.bin

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