Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
+
+model-index:
+  - name: Qwen2-Simple-Arguments
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          name: Argument-parsing
+          type: Argument-parsing
+        metrics:
+          - name: Accuracy
+            type: Accuracy
+            value: 100
+---
+# Qwen2 Simple Arguments
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model aims to parse simple english arguments, arguments formed of two premises and a conclusion, including two propositions.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Cristian Desivo
+- **Model type:** LLM
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model:** Qwen2-0.5b
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** TBD
+- **Paper:** TBD
+- **Demo:** TBD
+
+## Usage
+
+Below we share some code snippets on how to get quickly started with running the model.
+
+### transformers 🤗
+First make sure to pip install -U transformers, then use the code below replacing the `argument` variable for the argument you want to parse:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("./model_gguf", 
+    device_map="auto",)
+tokenizer = AutoTokenizer.from_pretrained("./model_gguf")
+
+argument = "If it's wednesday it's cold, and it's cold, therefore it's wednesday."
+
+instruction = 'Based on the following argument, identify the following elements: premises, conclusion, propositions, type of argument, negation of propositions and validity.'
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:"""
+prompt = alpaca_prompt.format(instruction, argument)
+input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_length=1000, num_return_sequences=1)
+print(tokenizer.decode(outputs[0]))
+```
+Output:
+```
+Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+Based on the following argument, identify the following elements: premises, conclusion, propositions, type of argument, negation of propositions and validity.
+
+### Input:
+If it's wednesday it's cold, and it's cold, therefore it's wednesday.
+
+### Response: 
+{"Premise 1": "If it's wednesday it's cold",
+"Premise 2": "It's cold",
+"Conclusion": "It is Wednesday",
+"Proposition 1": "It is Wednesday",
+"Proposition 2": "It is cold",
+"Type of argument": "affirming the consequent",
+"Negation of Proposition 1": "It is not Wednesday",
+"Negation of Proposition 2": "It is not cold",
+"Validity": "false"}<|endoftext|>
+```
+
+
+## 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. -->
+
+The model was trained on syntethic data, based on the following types of arguments: 
+- Modus Ponen
+- Modus Tollen
+- Affirming Consequent
+- Disjunctive Syllogism
+- Denying Antecedent
+- Invalid Conditional Syllogism
+
+Each argument was constructed by selecting two random propositions (from a list of 400 propositions that was generated beforehand), choosing a type of argument and combining it all with randomly selected connectors (therefore, since, hence, thus, etc).
+
+
+
+### 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]