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Duplicate from aidansys17/s2zs-p1o2-zl4f-0
Browse filesCo-authored-by: Aidan Flynn <aidansys17@users.noreply.huggingface.co>
- .gitattributes +35 -0
- README.md +40 -0
- adapter_config.json +23 -0
- adapter_model.safetensors +3 -0
- checkpoint-41025/README.md +239 -0
- checkpoint-41025/adapter_config.json +23 -0
- checkpoint-41025/adapter_model.bin +3 -0
- checkpoint-41025/adapter_model.safetensors +3 -0
- checkpoint-41025/optimizer.pt +3 -0
- checkpoint-41025/pytorch_model.bin +3 -0
- checkpoint-41025/rng_state.pth +3 -0
- checkpoint-41025/scheduler.pt +3 -0
- checkpoint-41025/special_tokens_map.json +24 -0
- checkpoint-41025/tokenizer.json +0 -0
- checkpoint-41025/tokenizer.model +3 -0
- checkpoint-41025/tokenizer_config.json +39 -0
- checkpoint-41025/trainer_state.json +109 -0
- checkpoint-41025/training_args.bin +3 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +39 -0
- training_args.bin +3 -0
- training_params.json +1 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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tags:
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- autotrain
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- text-generation
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widget:
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- text: "I love AutoTrain because "
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license: other
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---
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# Model Trained Using AutoTrain
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This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
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# Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "PATH_TO_THIS_REPO"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map="auto",
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torch_dtype='auto'
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).eval()
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# Prompt content: "hi"
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messages = [
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{"role": "user", "content": "hi"}
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]
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input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
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output_ids = model.generate(input_ids.to('cuda'))
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
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# Model response: "Hello! How can I assist you today?"
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print(response)
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```
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "meta-llama/Llama-2-7b-hf",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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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 sha256:8c5ec275093fe856c386a4c71a08f30bd2f9708a86cb355a443a9884c052119d
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size 33571624
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checkpoint-41025/README.md
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---
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library_name: peft
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base_model: meta-llama/Llama-2-7b-hf
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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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|
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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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|
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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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|
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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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195 |
+
[More Information Needed]
|
196 |
+
|
197 |
+
## Model Card Contact
|
198 |
+
|
199 |
+
[More Information Needed]
|
200 |
+
|
201 |
+
|
202 |
+
## Training procedure
|
203 |
+
|
204 |
+
|
205 |
+
The following `bitsandbytes` quantization config was used during training:
|
206 |
+
- quant_method: bitsandbytes
|
207 |
+
- load_in_8bit: False
|
208 |
+
- load_in_4bit: True
|
209 |
+
- llm_int8_threshold: 6.0
|
210 |
+
- llm_int8_skip_modules: None
|
211 |
+
- llm_int8_enable_fp32_cpu_offload: False
|
212 |
+
- llm_int8_has_fp16_weight: False
|
213 |
+
- bnb_4bit_quant_type: nf4
|
214 |
+
- bnb_4bit_use_double_quant: False
|
215 |
+
- bnb_4bit_compute_dtype: float16
|
216 |
+
|
217 |
+
### Framework versions
|
218 |
+
|
219 |
+
|
220 |
+
- PEFT 0.6.2
|
221 |
+
## Training procedure
|
222 |
+
|
223 |
+
|
224 |
+
The following `bitsandbytes` quantization config was used during training:
|
225 |
+
- quant_method: bitsandbytes
|
226 |
+
- load_in_8bit: False
|
227 |
+
- load_in_4bit: True
|
228 |
+
- llm_int8_threshold: 6.0
|
229 |
+
- llm_int8_skip_modules: None
|
230 |
+
- llm_int8_enable_fp32_cpu_offload: False
|
231 |
+
- llm_int8_has_fp16_weight: False
|
232 |
+
- bnb_4bit_quant_type: nf4
|
233 |
+
- bnb_4bit_use_double_quant: False
|
234 |
+
- bnb_4bit_compute_dtype: float16
|
235 |
+
|
236 |
+
### Framework versions
|
237 |
+
|
238 |
+
|
239 |
+
- PEFT 0.6.2
|
checkpoint-41025/adapter_config.json
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"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "<s>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "</s>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
}
|
27 |
+
},
|
28 |
+
"bos_token": "<s>",
|
29 |
+
"clean_up_tokenization_spaces": false,
|
30 |
+
"eos_token": "</s>",
|
31 |
+
"legacy": false,
|
32 |
+
"model_max_length": 2048,
|
33 |
+
"pad_token": "</s>",
|
34 |
+
"padding_side": "right",
|
35 |
+
"sp_model_kwargs": {},
|
36 |
+
"tokenizer_class": "LlamaTokenizer",
|
37 |
+
"unk_token": "<unk>",
|
38 |
+
"use_default_system_prompt": false
|
39 |
+
}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e0912c6c7cca8f4ef282a51c6098c82234e97a1ec62d83def91eba173944ff5c
|
3 |
+
size 4600
|
training_params.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"model": "meta-llama/Llama-2-7b-hf", "data_path": "aidansys17/autotrain-data-s2zs-p1o2-zl4f", "project_name": "/tmp/model", "train_split": "train", "valid_split": null, "text_column": "autotrain_text", "rejected_text_column": "autotrain_rejected_text", "lr": 3e-05, "epochs": 3, "batch_size": 2, "warmup_ratio": 0.1, "gradient_accumulation": 1, "optimizer": "adamw_torch", "scheduler": "linear", "weight_decay": 0.0, "max_grad_norm": 1.0, "seed": 42, "add_eos_token": true, "block_size": 1024, "use_peft": true, "lora_r": 16, "lora_alpha": 32, "lora_dropout": 0.05, "logging_steps": -1, "evaluation_strategy": "epoch", "save_total_limit": 1, "save_strategy": "epoch", "auto_find_batch_size": false, "fp16": true, "push_to_hub": true, "use_int8": false, "model_max_length": 2048, "repo_id": "aidansys17/s2zs-p1o2-zl4f-0", "use_int4": true, "trainer": "default", "target_modules": "", "merge_adapter": false, "username": "aidansys17", "use_flash_attention_2": false, "log": "none", "disable_gradient_checkpointing": false, "model_ref": null, "dpo_beta": 0.1, "prompt_text_column": "autotrain_prompt"}
|