Instructions to use Manikanta21/OceanGPT-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Manikanta21/OceanGPT-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("zjunlp/OceanGPT-basic-7B-v0.1") model = PeftModel.from_pretrained(base_model, "Manikanta21/OceanGPT-v1") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +207 -0
- adapter_config.json +45 -0
- adapter_model.safetensors +3 -0
- optimizer.pt +3 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +16 -0
- trainer_state.json +496 -0
- training_args.bin +3 -0
README.md
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| 1 |
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---
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base_model: zjunlp/OceanGPT-basic-7B-v0.1
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:zjunlp/OceanGPT-basic-7B-v0.1
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- lora
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- transformers
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.19.1
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adapter_config.json
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{
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| 2 |
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"alora_invocation_tokens": null,
|
| 3 |
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"alpha_pattern": {},
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| 4 |
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"arrow_config": null,
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| 5 |
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"auto_mapping": null,
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| 6 |
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"base_model_name_or_path": "zjunlp/OceanGPT-basic-7B-v0.1",
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| 7 |
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"bias": "none",
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| 8 |
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"corda_config": null,
|
| 9 |
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"ensure_weight_tying": false,
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| 10 |
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"eva_config": null,
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| 11 |
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"exclude_modules": null,
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| 12 |
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"fan_in_fan_out": false,
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| 13 |
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"inference_mode": true,
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| 14 |
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"init_lora_weights": true,
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| 15 |
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"layer_replication": null,
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| 16 |
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"layers_pattern": null,
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| 17 |
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"layers_to_transform": null,
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| 18 |
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"loftq_config": {},
|
| 19 |
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"lora_alpha": 32,
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| 20 |
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"lora_bias": false,
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| 21 |
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"lora_dropout": 0.005,
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| 22 |
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"lora_ga_config": null,
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| 23 |
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"megatron_config": null,
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| 24 |
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"megatron_core": "megatron.core",
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| 25 |
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"modules_to_save": null,
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| 26 |
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"peft_type": "LORA",
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| 27 |
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"peft_version": "0.19.1",
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| 28 |
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"qalora_group_size": 16,
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| 29 |
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"r": 16,
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| 30 |
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"rank_pattern": {},
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| 31 |
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"revision": null,
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"target_modules": [
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"k_proj",
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"v_proj",
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| 35 |
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"q_proj",
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| 36 |
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"o_proj"
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| 37 |
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],
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| 38 |
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"target_parameters": null,
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| 39 |
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"task_type": "CAUSAL_LM",
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| 40 |
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"trainable_token_indices": null,
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| 41 |
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"use_bdlora": null,
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"use_dora": false,
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| 43 |
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"use_qalora": false,
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| 44 |
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"use_rslora": false
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| 45 |
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:db1f7af64279cb17caf339d695918fedb116f62214d4b5482d3b220f5b068294
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size 67143296
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optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:649e6ff8b1a98f6e661585554cfd1604eec9f71f9262eca5f9f3378007d702eb
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size 134437579
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rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:971c2f17cbbba362b6c5b7b88fdb6b30718c1a2ae2349aefaf919929c25a3955
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size 14645
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scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bd14ca24e175c1c58ad59e8089bbffbb10d6e090d5848b00ffb24fd47a5a160
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size 1465
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tokenizer.json
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tokenizer_config.json
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{
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|
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