Model save

README.md

@@ -1,204 +1,129 @@
 ---
+license: apache-2.0
 library_name: peft
+tags:
+- generated_from_trainer
 base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
+model-index:
+- name: empower-functions-more-tools-parallel
+  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: `0.4.0`
+```yaml
+base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
+model_type: AutoModelForCausalLM
+tokenizer_type: LlamaTokenizer
+trust_remote_code: true
 
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+chat_template: inst
 
-## 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]
+datasets:
+  - path: ./data/with_function_response/more_functions/function_used_training.jsonl
+    type: sharegpt
+    conversation: mistral
+  - path: ./data/with_function_response/more_functions/function_not_used_training.jsonl
+    type: sharegpt
+    conversation: mistral    
+  - path: ./data/with_function_response/parallel_call/missing_parameter_data_training.jsonl
+    type: sharegpt
+    conversation: mistral
+  - path: ./data/with_function_response/parallel_call/parallel_data_training.jsonl
+    type: sharegpt
+    conversation: mistral
 
-## Technical Specifications [optional]
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ../empower-functions-more-tools-parallel
 
-### Model Architecture and Objective
+model_config:
+  output_router_logits: true
 
-[More Information Needed]
+adapter: qlora
+lora_model_dir:
 
-### Compute Infrastructure
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
 
-[More Information Needed]
+lora_r: 32
+lora_alpha: 64
+lora_dropout: 0.05
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
 
-#### Hardware
 
-[More Information Needed]
+wandb_project: empower-functions
+wandb_name: empower-functions-more-tools-parallel
+wandb_log_model: end
+hub_model_id: dyang415/empower-functions-more-tools-parallel
 
-#### Software
 
-[More Information Needed]
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
 
-## Citation [optional]
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
 
-**BibTeX:**
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
 
-[More Information Needed]
+warmup_steps: 10
 
-**APA:**
+eval_table_size:
+eval_max_new_tokens: 256
+eval_steps: 0.05
+save_steps: 0.1
+debug:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+```
 
-[More Information Needed]
+</details><br>
 
-## Glossary [optional]
+# empower-functions-more-tools-parallel
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset.
 
-[More Information Needed]
+## Model description
 
-## More Information [optional]
+More information needed
 
-[More Information Needed]
+## Intended uses & limitations
 
-## Model Card Authors [optional]
+More information needed
 
-[More Information Needed]
+## Training and evaluation data
 
-## Model Card Contact
+More information needed
 
-[More Information Needed]
 ## Training procedure
 
+
 The following `bitsandbytes` quantization config was used during training:
 - quant_method: QuantizationMethod.BITS_AND_BYTES
 - load_in_8bit: False
@@ -211,6 +136,27 @@ The following `bitsandbytes` quantization config was used during training:
 - bnb_4bit_use_double_quant: True
 - bnb_4bit_compute_dtype: bfloat16
 
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- total_eval_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 4
+
 ### Framework versions
 
-- PEFT 0.7.0
+- PEFT 0.7.0
+- Transformers 4.37.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.17.1
+- Tokenizers 0.15.0

adapter_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:f4c73cafb961cb8103ef191f331b9cc32c93b6d707b000edce4448e240adeaa7
 size 109086416