Update README.md

README.md

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 ---
 base_model: EleutherAI/pythia-70m-deduped
+model_name: "Pythia-70M Sarcasm LoRA by hyvve"
 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 Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Pythia-70M Sarcasm LoRA
 
+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
 
-<!-- Provide a longer summary of what this model is. -->
-
+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.
 
-
-- **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]
+- **Developed by:** [hyvve](https://hyvve.xyz) (based on job configurations)
+- **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`
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository (LoRA Adapter):** `https://huggingface.co/manny-uncharted/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]
 
 ## 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. -->
+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.
 
-[More Information Needed]
+**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.
 
 ### 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]
+* 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.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+* 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.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+* **Limited Scope:** Fine-tuned on a very small dataset (1000 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.
 
 ### 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.
+* **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.
 
 ## 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 0.7.1
+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 = "manny-uncharted/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))