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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-6384/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,306 @@
+
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+- google/gemma
+- PyTorch
+- transformers
+- trl
+- peft
+- tensorboard
+base_model: google/gemma-2b
+widget:
+  - example_title: Pirate!
+    messages:
+      - role: system
+        content: You are a pirate chatbot who always responds with Arr!
+      - role: user
+        content: "There's a llama on my lawn, how can I get rid of him?"
+    output:
+      text: >-
+        Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare
+        sight, but I've got a plan that might help ye get rid of 'im. Ye'll need
+        to gather some carrots and hay, and then lure the llama away with the
+        promise of a tasty treat. Once he's gone, ye can clean up yer lawn and
+        enjoy the peace and quiet once again. But beware, me hearty, for there
+        may be more llamas where that one came from! Arr!
+model-index:
+- name: gemma-2b-chat
+  results: []
+datasets:
+- HuggingFaceH4/deita-10k-v0-sft
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Model Card for gemma-2b-chat:
+
+**gemma-2b-chat** is a language model that is trained to act as helpful assistant. It is a finetuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) that was trained using `SFTTrainer` on publicly available dataset [
+HuggingFaceH4/deita-10k-v0-sft](https://huggingface.co/datasets/HuggingFaceH4/deita-10k-v0-sft).
+
+## Training Procedure:
+
+The training code used to create this model was generated by [Menouar/LLM-FineTuning-Notebook-Generator](https://huggingface.co/spaces/Menouar/LLM-FineTuning-Notebook-Generator).
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during the training:
+
+
+- output_dir: temp_gemma-2b-chat
+ 
+- overwrite_output_dir: True
+ 
+- do_train: False
+ 
+- do_eval: False
+ 
+- do_predict: False
+ 
+- evaluation_strategy: no
+ 
+- prediction_loss_only: False
+ 
+- per_device_train_batch_size: 3
+ 
+- per_device_eval_batch_size: 8
+ 
+- per_gpu_train_batch_size: None
+ 
+- per_gpu_eval_batch_size: None
+ 
+- gradient_accumulation_steps: 2
+ 
+- eval_accumulation_steps: None
+ 
+- eval_delay: 0
+ 
+- learning_rate: 2e-05
+ 
+- weight_decay: 0.0
+ 
+- adam_beta1: 0.9
+ 
+- adam_beta2: 0.999
+ 
+- adam_epsilon: 1e-08
+ 
+- max_grad_norm: 0.3
+ 
+- num_train_epochs: 1
+ 
+- max_steps: -1
+ 
+- lr_scheduler_type: cosine
+ 
+- lr_scheduler_kwargs: {}
+ 
+- warmup_ratio: 0.1
+ 
+- warmup_steps: 0
+ 
+- log_level: passive
+ 
+- log_level_replica: warning
+ 
+- log_on_each_node: True
+ 
+- logging_dir: temp_gemma-2b-chat/runs/Mar11_17-14-25_f4965e0005f4
+ 
+- logging_strategy: steps
+ 
+- logging_first_step: False
+ 
+- logging_steps: 10
+ 
+- logging_nan_inf_filter: True
+ 
+- save_strategy: epoch
+ 
+- save_steps: 500
+ 
+- save_total_limit: None
+ 
+- save_safetensors: True
+ 
+- save_on_each_node: False
+ 
+- save_only_model: False
+ 
+- no_cuda: False
+ 
+- use_cpu: False
+ 
+- use_mps_device: False
+ 
+- seed: 42
+ 
+- data_seed: None
+ 
+- jit_mode_eval: False
+ 
+- use_ipex: False
+ 
+- bf16: True
+ 
+- fp16: False
+ 
+- fp16_opt_level: O1
+ 
+- half_precision_backend: auto
+ 
+- bf16_full_eval: False
+ 
+- fp16_full_eval: False
+ 
+- tf32: None
+ 
+- local_rank: 0
+ 
+- ddp_backend: None
+ 
+- tpu_num_cores: None
+ 
+- tpu_metrics_debug: False
+ 
+- debug: []
+ 
+- dataloader_drop_last: False
+ 
+- eval_steps: None
+ 
+- dataloader_num_workers: 0
+ 
+- dataloader_prefetch_factor: None
+ 
+- past_index: -1
+ 
+- run_name: temp_gemma-2b-chat
+ 
+- disable_tqdm: False
+ 
+- remove_unused_columns: True
+ 
+- label_names: None
+ 
+- load_best_model_at_end: False
+ 
+- metric_for_best_model: None
+ 
+- greater_is_better: None
+ 
+- ignore_data_skip: False
+ 
+- fsdp: []
+ 
+- fsdp_min_num_params: 0
+ 
+- fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+ 
+- fsdp_transformer_layer_cls_to_wrap: None
+ 
+- accelerator_config: AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True)
+ 
+- deepspeed: None
+ 
+- label_smoothing_factor: 0.0
+ 
+- optim: adamw_torch_fused
+ 
+- optim_args: None
+ 
+- adafactor: False
+ 
+- group_by_length: False
+ 
+- length_column_name: length
+ 
+- report_to: ['tensorboard']
+ 
+- ddp_find_unused_parameters: None
+ 
+- ddp_bucket_cap_mb: None
+ 
+- ddp_broadcast_buffers: None
+ 
+- dataloader_pin_memory: True
+ 
+- dataloader_persistent_workers: False
+ 
+- skip_memory_metrics: True
+ 
+- use_legacy_prediction_loop: False
+ 
+- push_to_hub: False
+ 
+- resume_from_checkpoint: None
+ 
+- hub_model_id: None
+ 
+- hub_strategy: every_save
+ 
+- hub_token: None
+ 
+- hub_private_repo: False
+ 
+- hub_always_push: False
+ 
+- gradient_checkpointing: True
+ 
+- gradient_checkpointing_kwargs: {'use_reentrant': False}
+ 
+- include_inputs_for_metrics: False
+ 
+- fp16_backend: auto
+ 
+- push_to_hub_model_id: None
+ 
+- push_to_hub_organization: None
+ 
+- push_to_hub_token: None
+ 
+- mp_parameters: 
+ 
+- auto_find_batch_size: False
+ 
+- full_determinism: False
+ 
+- torchdynamo: None
+ 
+- ray_scope: last
+ 
+- ddp_timeout: 1800
+ 
+- torch_compile: False
+ 
+- torch_compile_backend: None
+ 
+- torch_compile_mode: None
+ 
+- dispatch_batches: None
+ 
+- split_batches: None
+ 
+- include_tokens_per_second: False
+ 
+- include_num_input_tokens_seen: False
+ 
+- neftune_noise_alpha: None
+ 
+- distributed_state: Distributed environment: NO
+Num processes: 1
+Process index: 0
+Local process index: 0
+Device: cuda
+
+ 
+- _n_gpu: 1
+ 
+- __cached__setup_devices: cuda:0
+ 
+- deepspeed_plugin: None
+ 

checkpoint-6384/README.md

@@ -0,0 +1,204 @@
+---
+library_name: peft
+base_model: google/gemma-2b
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## 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 0.8.2

checkpoint-6384/adapter_config.json

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+  "base_model_name_or_path": "google/gemma-2b",
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 6,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "q_proj",
+    "down_proj",
+    "up_proj",
+    "o_proj",
+    "v_proj",
+    "gate_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_rslora": false
+}

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+    "<|im_end|>"
+  ],
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+  ],
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