Sandiago21
/

llama-13b-hf-prompt-answering

@@ -6,52 +6,52 @@ library_name: transformers
 pipeline_tag: conversational
 ---
-Model Card for Model ID
 Finetuned depacoda-research/llamma-13b-hf on conversations
-Model Details
-Model Description
 The depacoda-research/llamma-13b-hf model was finetuned on conversations and question answering prompts
-Developed by: [More Information Needed]
-Shared by [optional]: [More Information Needed]
-Model type: Causal LM
-Language(s) (NLP): English, multilingual
-License: Research
-Finetuned from model [optional]: depacoda-research/llamma-13b-hf
-Model Sources [optional]
-Repository: [More Information Needed]
-Paper [optional]: [More Information Needed]
-Demo [optional]: [More Information Needed]
-Uses
 The model can be used for prompt answering
-Direct Use
 The model can be used for prompt answering
-Downstream Use [optional]
 Generating text and prompt answering
-Recommendations
 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
 Use the code below to get started with the model.
@@ -67,19 +67,19 @@ model = LlamaForCausalLM.from_pretrained(MODEL_NAME, load_in_8bit=True, device_m
 model = PeftModel.from_pretrained(model, "Sandiago21/public-ai-model")
 ```
-Training Details
-Training Data
 The decapoda-research/llama-13b-hf was finetuned on conversations and question answering data
-Training Procedure
 The decapoda-research/llama-13b-hf model was further trained and finetuned on question answering and prompts data
-Model Architecture and Objective
 The model is based on decapoda-research/llama-13b-hf model and finetuned adapters on top of the main model on conversations and question answering data.

 pipeline_tag: conversational
 ---
+## Model Card for Model ID
 Finetuned depacoda-research/llamma-13b-hf on conversations
+## Model Details
+### Model Description
 The depacoda-research/llamma-13b-hf model was finetuned on conversations and question answering prompts
+**Developed by:** [More Information Needed]
+**Shared by:** [More Information Needed]
+**Model type:** Causal LM
+**Language(s) (NLP):** English, multilingual
+**License:** Research
+**Finetuned from model:** depacoda-research/llamma-13b-hf
+## Model Sources [optional]
+**Repository:** [More Information Needed]
+**Paper:** [More Information Needed]
+**Demo:** [More Information Needed]
+## Uses
 The model can be used for prompt answering
+### Direct Use
 The model can be used for prompt answering
+### Downstream Use
 Generating text and prompt answering
+## Recommendations
 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
 Use the code below to get started with the model.
 model = PeftModel.from_pretrained(model, "Sandiago21/public-ai-model")
 ```
+## Training Details
+### Training Data
 The decapoda-research/llama-13b-hf was finetuned on conversations and question answering data
+### Training Procedure
 The decapoda-research/llama-13b-hf model was further trained and finetuned on question answering and prompts data
+## Model Architecture and Objective
 The model is based on decapoda-research/llama-13b-hf model and finetuned adapters on top of the main model on conversations and question answering data.