End of training

.gitattributes

@@ -25,6 +25,7 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,152 +1,154 @@
 ---
-pipeline_tag: image-to-text
+library_name: peft
+license: apache-2.0
+base_model: unsloth/mistral-7b-instruct-v0.2
 tags:
-- image-captioning
-languages:
-- en
-license: bsd-3-clause
+- axolotl
+- generated_from_trainer
+model-index:
+- name: bbdecd00-d8b9-464e-9454-e600bc6d1772
+  results: []
 ---
 
-# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
+<!-- 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. -->
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.1`
+```yaml
+adapter: lora
+base_model: unsloth/mistral-7b-instruct-v0.2
+bf16: auto
+chat_template: llama3
+dataset_prepared_path: null
+datasets:
+- data_files:
+  - 424a669f72441b3b_train_data.json
+  ds_type: json
+  format: custom
+  path: /workspace/input_data/424a669f72441b3b_train_data.json
+  type:
+    field_input: intent
+    field_instruction: instruction
+    field_output: response
+    format: '{instruction} {input}'
+    no_input_format: '{instruction}'
+    system_format: '{system}'
+    system_prompt: ''
+debug: null
+deepspeed: null
+early_stopping_patience: null
+eval_max_new_tokens: 128
+eval_table_size: null
+evals_per_epoch: 5
+flash_attention: true
+fp16: null
+fsdp: null
+fsdp_config: null
+gradient_accumulation_steps: 4
+gradient_checkpointing: false
+group_by_length: false
+hub_model_id: duyphu/bbdecd00-d8b9-464e-9454-e600bc6d1772
+hub_repo: null
+hub_strategy: checkpoint
+hub_token: null
+learning_rate: 0.0001
+load_in_4bit: false
+load_in_8bit: false
+local_rank: null
+logging_steps: 5
+lora_alpha: 16
+lora_dropout: 0.05
+lora_fan_in_fan_out: null
+lora_model_dir: null
+lora_r: 8
+lora_target_linear: true
+lr_scheduler: cosine
+max_steps: 50
+micro_batch_size: 2
+mlflow_experiment_name: /tmp/424a669f72441b3b_train_data.json
+model_type: AutoModelForCausalLM
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+output_dir: miner_id_24
+pad_to_sequence_len: true
+resume_from_checkpoint: null
+s2_attention: null
+sample_packing: false
+saves_per_epoch: 4
+sequence_len: 512
+strict: false
+tf32: false
+tokenizer_type: AutoTokenizer
+train_on_inputs: false
+trust_remote_code: true
+val_set_size: 0.05
+wandb_entity: null
+wandb_mode: online
+wandb_name: bbdecd00-d8b9-464e-9454-e600bc6d1772
+wandb_project: Gradients-On-Demand
+wandb_run: your_name
+wandb_runid: bbdecd00-d8b9-464e-9454-e600bc6d1772
+warmup_steps: 10
+weight_decay: 0.0
+xformers_attention: null
 
-Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone).
-
-| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
-|:--:|
-| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
-
-## TL;DR
-
-Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
-
-*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
-
-## Usage
-
-You can use this model for conditional and un-conditional image captioning
-
-### Using the Pytorch model
-
-#### Running the model on CPU
-
-<details>
-<summary> Click to expand </summary>
-
-```python
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-
-img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
-raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
-
-# conditional image captioning
-text = "a photography of"
-inputs = processor(raw_image, text, return_tensors="pt")
-
-out = model.generate(**inputs)
-print(processor.decode(out[0], skip_special_tokens=True))
-# >>> a photography of a woman and her dog
-
-# unconditional image captioning
-inputs = processor(raw_image, return_tensors="pt")
-
-out = model.generate(**inputs)
-print(processor.decode(out[0], skip_special_tokens=True))
->>> a woman sitting on the beach with her dog
 ```
-</details>
-
-#### Running the model on GPU
-
-##### In full precision 
 
-<details>
-<summary> Click to expand </summary>
+</details><br>
 
-```python
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
+# bbdecd00-d8b9-464e-9454-e600bc6d1772
 
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to("cuda")
+This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5076
 
-img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
-raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+## Model description
 
-# conditional image captioning
-text = "a photography of"
-inputs = processor(raw_image, text, return_tensors="pt").to("cuda")
+More information needed
 
-out = model.generate(**inputs)
-print(processor.decode(out[0], skip_special_tokens=True))
-# >>> a photography of a woman and her dog
+## Intended uses & limitations
 
-# unconditional image captioning
-inputs = processor(raw_image, return_tensors="pt").to("cuda")
+More information needed
 
-out = model.generate(**inputs)
-print(processor.decode(out[0], skip_special_tokens=True))
->>> a woman sitting on the beach with her dog
-```
-</details>
-
-##### In half precision (`float16`)
-
-<details>
-<summary> Click to expand </summary>
+## Training and evaluation data
 
-```python
-import torch
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
+More information needed
 
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
+## Training procedure
 
-img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
-raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+### Training hyperparameters
 
-# conditional image captioning
-text = "a photography of"
-inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- training_steps: 50
 
-out = model.generate(**inputs)
-print(processor.decode(out[0], skip_special_tokens=True))
-# >>> a photography of a woman and her dog
+### Training results
 
-# unconditional image captioning
-inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| No log        | 0.0000 | 1    | 0.6332          |
+| 2.2524        | 0.0004 | 10   | 0.5776          |
+| 2.1579        | 0.0008 | 20   | 0.5359          |
+| 2.0424        | 0.0013 | 30   | 0.5147          |
+| 1.8087        | 0.0017 | 40   | 0.5086          |
+| 1.9481        | 0.0021 | 50   | 0.5076          |
 
-out = model.generate(**inputs)
-print(processor.decode(out[0], skip_special_tokens=True))
->>> a woman sitting on the beach with her dog
-```
-</details>
 
-## BibTex and citation info
+### Framework versions
 
-```
-@misc{https://doi.org/10.48550/arxiv.2201.12086,
-  doi = {10.48550/ARXIV.2201.12086},
-  
-  url = {https://arxiv.org/abs/2201.12086},
-  
-  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
-  
-  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  
-  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
-  
-  publisher = {arXiv},
-  
-  year = {2022},
-  
-  copyright = {Creative Commons Attribution 4.0 International}
-}
-```
+- PEFT 0.13.2
+- Transformers 4.46.0
+- Pytorch 2.5.0+cu124
+- Datasets 3.0.1
+- Tokenizers 0.20.1

adapter_config.json

@@ -0,0 +1,34 @@
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+  "bias": "none",
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+  "init_lora_weights": true,
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+    "v_proj",
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+    "o_proj",
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+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

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 }

last-checkpoint/README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/mistral-7b-instruct-v0.2
+library_name: peft
+---
+
+# 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 0.13.2

last-checkpoint/adapter_config.json

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