Update README.md

README.md

@@ -1,13 +1,57 @@
 ---
 tags:
-- generated_from_trainer
+- physics
+- cosmology
 model-index:
-- name: workspace/output/cosmosage_qa
+- name: cosmosage_qa
   results: []
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+base_model: mistralai/Mistral-7B-v0.1
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# cosmosage
+
+Cosmosage is a natural-language cosmology assistant that can answer questions about cosmology.
+
+cosmosage_v2 first underwent continued pretraining based on thousands of papers and textbooks, 
+and was subsequently fine-tuned on synthetically-generated question-answer pairs. It is a full
+chat model, though it excels in Q&A mode, where the model gives a single answer in response to 
+a single question.
+
+The code used to generate cosmosage_v2 is available at https://github.com/tijmen/cosmosage
+
+## Usage
+
+After downloading cosmosage_v2, the following example code can be used to ask questions:
+
+```path_to_model = 'cosmosage_v2/'
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained(path_to_model).to(device)
+tokenizer = AutoTokenizer.from_pretrained(path_to_model)
+def ask_cosmosage(question):
+    input_ids = torch.cat([
+    tokenizer.encode("You are cosmosage, an AI programmed to be a cosmology expert. You answer the USER's question clearly in long form, always providing context. When appropriate, provide a reference.", return_tensors="pt"),
+    torch.tensor([[28705]]),
+    tokenizer.encode("USER:", add_special_tokens=False, return_tensors="pt"),
+    tokenizer.encode(question, add_special_tokens=False, return_tensors="pt"),
+    torch.tensor([[28705]]),
+    tokenizer.encode("ASSISTANT:", add_special_tokens=False, return_tensors="pt")
+    ], dim=-1).to(device)
+    generated_ids = model.generate(input_ids, max_length=input_ids.shape[1] + 1000, do_sample=True)
+    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)```
+
+## Comparison to cosmosage_v1
+
+cosmosage_v2 is a more knowledgeable model than cosmosage_v1 due to being pretrained on the papers and
+textbooks, rather than just on synthetically generated QA pairs. However, it continues to struggle with 
+_reliability_. While many of its answers are factually accurate, some are not. The outputs of cosmosage 
+(or any LLM) should not be trusted to be factual.
 
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 <details><summary>See axolotl config</summary>
@@ -178,4 +222,4 @@ The following hyperparameters were used during training:
 - Transformers 4.38.0.dev0
 - Pytorch 2.0.1+cu118
 - Datasets 2.17.0
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0