Create mode_card.yaml

mode_card.yaml

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+model-index:
+- name: LLAMA 7B Sentiment Analysis Adapter
+  results:
+    - task:
+        name: Sentiment Analysis
+        type: text-classification
+      dataset:
+        name: Amazon Sentiment Review dataset
+        type: amazon_reviews
+model-metadata:
+  license: apache-2.0
+  library_name: transformers
+  tags: ["text-classification", "sentiment-analysis", "English"]
+  languages: ["en"]
+  widget:
+    - text: "I love using FuturixAI for my daily tasks!"
+
+intended-use:
+  primary-uses:
+    - This model is intended for sentiment analysis on English language text.
+  primary-users:
+    - Researchers
+    - Social media monitoring tools
+    - Customer feedback analysis systems
+
+training-data:
+  training-data-source: Amazon Sentiment Review dataset
+
+quantitative-analyses:
+  use-cases-limitations:
+    - The model may perform poorly on texts that contain a lot of slang or are in a different language than it was trained on.
+
+ethical-considerations:
+  risks-and-mitigations:
+    - There is a risk of the model reinforcing or creating biases based on the training data. Users should be aware of this and consider additional bias mitigation strategies when using the model.
+
+model-architecture:
+  architecture: LLAMA 7B with LORA adaptation
+  library: PeftModel
+
+how-to-use:
+  installation:
+    - pip install transformers peft
+  code-examples:
+    - |
+      ```python
+      import transformers
+      from peft import PeftModel
+
+      model_name = "meta-llama/Llama-2-7b"  # you can use VICUNA 7B model as well
+      peft_model_id = "Futurix-AI/LLAMA_7B_Sentiment_Analysis_Amazon_Review_Dataset"
+
+      tokenizer_t5 = transformers.AutoTokenizer.from_pretrained(model_name)
+      model_t5 = transformers.AutoModelForCausalLM.from_pretrained(model_name)
+      model_t5 = PeftModel.from_pretrained(model_t5, peft_model_id)
+
+      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:
+      Detect the sentiment of the tweet.
+      ###Input:
+      FuturixAI embodies the spirit of innovation, with a resolve to push the boundaries of what's possible through science and technology. 
+      ###Response:
+      """
+
+      inputs = tokenizer_t5(prompt, return_tensors="pt")
+      for k, v in inputs.items():
+          inputs[k] = v
+      outputs = model_t5.generate(**inputs, max_length=256, do_sample=True)
+      text = tokenizer_t5.batch_decode(outputs, skip_special_tokens=True)[0]
+      print(text)
+      ```