End of training

README.md

@@ -1,242 +1,158 @@
 ---
+license: mit
 library_name: peft
+tags:
+- axolotl
+- generated_from_trainer
 base_model: 152334H/miqu-1-70b-sf
+model-index:
+- name: miqu-limarp-70b
+  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: `0.3.0`
+```yaml
+base_model: 152334H/miqu-1-70b-sf
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
 
+load_in_8bit: false
+load_in_4bit: true
+strict: false
 
-## 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]
+datasets:
+  - path: NobodyExistsOnTheInternet/LimaRP
+    type: sharegpt
+    conversation: chatml
+  - path: Doctor-Shotgun/no-robots-sharegpt
+    type: sharegpt
+    conversation: chatml
 
-### Model Architecture and Objective
+chat_template: chatml
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./miqu-lora
+save_safetensors: true
 
-[More Information Needed]
+adapter: qlora
+lora_model_dir:
 
-### Compute Infrastructure
+sequence_len: 8192
+sample_packing: true
 
-[More Information Needed]
+lora_r: 64
+lora_alpha: 128
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
 
-#### Hardware
+wandb_project: miqu-lora
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
 
-[More Information Needed]
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: paged_lion_8bit
+lr_scheduler: cosine
+learning_rate: 0.00025
 
-#### Software
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
 
-[More Information Needed]
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+auto_resume_from_checkpoints: true
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+save_total_limit: 2
 
-## Citation [optional]
+warmup_steps: 10
+eval_table_size:
+weight_decay: 0
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "<|im_end|>"
+  unk_token: "</s>"
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+tokens:
+    - "<|im_start|>"
+    - "<|im_end|>"
 
-[More Information Needed]
+neftune_noise_alpha: 5
 
-**APA:**
 
-[More Information Needed]
+hub_model_id: NobodyExistsOnTheInternet/miqu-limarp-70b
+hub_strategy: all_checkpoints
+hf_use_auth_token: true
 
-## Glossary [optional]
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+</details><br>
 
-[More Information Needed]
+# miqu-limarp-70b
 
-## More Information [optional]
+This model is a fine-tuned version of [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) on the None dataset.
 
-[More Information Needed]
+## Model description
 
-## Model Card Authors [optional]
+More information needed
 
-[More Information Needed]
+## Intended uses & limitations
 
-## Model Card Contact
+More information needed
 
-[More Information Needed]
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
+### Training hyperparameters
 
-The following `bitsandbytes` quantization config was used during training:
-- quant_method: bitsandbytes
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: True
-- bnb_4bit_compute_dtype: bfloat16
-
-### Framework versions
-
+The following hyperparameters were used during training:
+- learning_rate: 0.00025
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- total_eval_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 4
 
-- PEFT 0.6.0
-## Training procedure
+### Training results
 
 
-The following `bitsandbytes` quantization config was used during training:
-- quant_method: bitsandbytes
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: True
-- bnb_4bit_compute_dtype: bfloat16
 
 ### Framework versions
 
-
-- PEFT 0.6.0
+- Transformers 4.37.0.dev0
+- Pytorch 2.0.1+cu117
+- Datasets 2.16.1
+- Tokenizers 0.15.0
 ## Training procedure
 
 

adapter_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:63c71c72f4fce5aab6b09b12652440851c78846364204693482bd780a9647cbd
+oid sha256:c4d592eccc4205c5953ef5d1ffe8b3e12266987f2cd7b3198723a40a46c1e670
 size 4362295248