Job completed: Upload fine-tuned LoRA adapter and artifacts from Space.

README.md

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 ---
 base_model: EleutherAI/pythia-70m-deduped
-model_name: "Pythia-70M Sarcasm LoRA by hyvve data platform"
 library_name: peft
-tags:
-  - text-generation
-  - lora
-  - peft
-  - sarcasm
-  - pythia
-  - fine-tuning
-  - causal-lm
-  - EleutherAI
-license: apache-2.0
-pipeline_tag: text-generation
 ---
 
-# Model Card for Pythia-70M Sarcasm LoRA
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-This model is a LoRA (Low-Rank Adaptation) fine-tune of the `EleutherAI/pythia-70m-deduped` model, specifically adapted for tasks related to sarcasm.
 
 ## Model Details
 
 ### Model Description
 
-This is a PEFT LoRA adapter for the `EleutherAI/pythia-70m-deduped` model. It has been fine-tuned on a dataset related to sarcasm. As a Causal Language Model (CLM), its primary function is to predict the next token in a sequence. This fine-tuning aims to imbue the model with an understanding or stylistic representation of sarcastic language.
+<!-- Provide a longer summary of what this model is. -->
+
 
-- **Developed by:** hyvve (based on job configurations)
-- **Funded by [optional]:** [Information Not Provided]
-- **Shared by [optional]:** hyvve
-- **Model type:** Causal Language Model (specifically, a LoRA adapter for a GPT-NeoX based model)
-- **Language(s) (NLP):** English (derived from the base model and assumed dataset language)
-- **License:** Apache-2.0 (inherited from the base model `EleutherAI/pythia-70m-deduped`)
-- **Finetuned from model:** `EleutherAI/pythia-70m-deduped`
+
+- **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]
 
-- **Repository (LoRA Adapter):** `https://huggingface.co/Testys/pythia-70m-sarcasm-lora` (based on `hf_target_model_repo_id`)
-- **Base Model Repository:** `https://huggingface.co/EleutherAI/pythia-70m-deduped`
-- **Paper [optional]:** For Pythia suite: [arXiv:2304.01373](https://arxiv.org/abs/2304.01373)
-- **Demo [optional]:** [Not Provided]
+<!-- 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 LoRA adapter is intended to be loaded on top of the `EleutherAI/pythia-70m-deduped` base model. It can be used for:
-* Generating text with a sarcastic tone or style.
-* Completing prompts in a sarcastic manner.
-* Research into modeling nuanced aspects of language like sarcasm with smaller LMs.
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-**Note:** Due to the extremely small dataset size used for fine-tuning (14 examples), the model's ability to robustly generate or understand sarcasm will be very limited. It primarily serves as a pipeline and integration test.
+[More Information Needed]
 
 ### Downstream Use [optional]
 
-* Further fine-tuning on larger, more diverse sarcasm datasets.
-* Integration into applications requiring conditional text generation with a sarcastic flavor (e.g., chatbots, creative writing tools), though extensive further tuning would be necessary.
+<!-- 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
 
-* Reliable sarcasm detection or classification without significant further development and evaluation.
-* Generating harmful, biased, or offensive content, even if framed as sarcasm.
-* Use in critical applications where misinterpretation of sarcasm could have negative consequences.
-* Generating fluent, coherent, and factually accurate long-form text beyond the capabilities of the 70M parameter base model.
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-* **Limited Scope:** Fine-tuned on a very small dataset (14 examples), so its understanding and generation of sarcasm will be superficial and not generalizable.
-* **Inherited Biases:** Inherits biases from the `EleutherAI/pythia-70m-deduped` base model, which was trained on The Pile. These can include societal, gender, and racial biases.
-* **Misinterpretation of Sarcasm:** Sarcasm is highly context-dependent and subjective. The model may generate text that is inappropriately sarcastic or fail to understand sarcastic prompts correctly.
-* **Potential for Harmful Sarcasm:** Sarcasm can be used to convey negativity or veiled aggression. The model might inadvertently generate such content.
-* **Numerical Instability:** During the logged training run, an `eval_loss: nan` was observed, indicating potential issues with evaluation on the tiny validation set or numerical instability under the given configuration. The `train_loss: 0.0` also suggests extreme overfitting or issues with the learning process on such limited data.
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
 
 ### Recommendations
 
-* **Thorough Evaluation:** Before any production use, the model (after further fine-tuning on a substantial dataset) would require rigorous evaluation for both sarcasm generation quality and potential biases.
-* **Content Moderation:** Downstream applications should implement content moderation and safety filters.
-* **Context is Key:** Use with clear context and be aware that its sarcastic capabilities are likely very brittle due to the limited training data.
-* **Do Not Use for Critical Decisions:** This model, in its current state, is not suitable for any critical applications.
+<!-- 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
 
-To use this LoRA adapter, you'll need to load the base model and then apply the adapter using the PEFT library.
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import torch
-
-base_model_id = "EleutherAI/pythia-70m-deduped"
-adapter_model_id = "Testys/pythia-70m-sarcasm-lora" # Replace with your actual model ID
-
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained(base_model_id)
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-# Load the base model (e.g., in 4-bit if that's how the adapter was trained/intended)
-# For QLoRA, BitsAndBytesConfig would be needed here as during training
-# For simplicity, this example loads without quantization. Adapt as needed.
-base_model = AutoModelForCausalLM.from_pretrained(
-    base_model_id,
-    # quantization_config=BitsAndBytesConfig(...) # Add if loading in 4-bit/8-bit
-    # torch_dtype=torch.float16, # Or torch.bfloat16
-    device_map="auto"
-)
-
-# Load the PEFT LoRA model (adapter)
-model = PeftModel.from_pretrained(base_model, adapter_model_id)
-model = model.merge_and_unload() # Optional: merge adapter into base model for faster inference
-
-# Now you can use the model for generation
-prompt = "The weather today is just " # Example prompt
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-# Generate text
-# Adjust generation parameters as needed
-outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+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 0.7.1

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