Update README.md

README.md

@@ -1,7 +1,13 @@
 ---
 license: llama2
+pipeline_tag: text-generation
+language:
+- en
+library_name: transformers
 ---
 
+Change from 1.1 -> 1.2: 20% more data than 1.1 and 2x training time.
+
 All Synthia models are uncensored. Please use it with caution and with best intentions. You are responsible for how you use Synthia.
 
 To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message:
@@ -9,6 +15,39 @@ To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use t
 Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
 ```
 
+# Synthia-70B-v1.2
+SynthIA (Synthetic Intelligent Agent) is a LLama-2-70B model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations.
+
+<br>
+
+![Synthia](https://huggingface.co/migtissera/Synthia-13B/resolve/main/Synthia.jpeg)
+
+<br>
+
+<br>
+
+#### License Disclaimer:
+
+This model is bound by the license & usage restrictions of the original Llama-2 model, and comes with no warranty or gurantees of any kind.
+
+<br>
+
+## Evaluation
+
+We evaluated Synthia-70B-v1.2 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. 
+
+Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+
+||||
+|:------:|:--------:|:-------:|
+|**Task**|**Metric**|**Value**|
+|*arc_challenge*|acc_norm|70.48|
+|*hellaswag*|acc_norm|86.98|
+|*mmlu*|acc_norm|70.13|
+|*truthfulqa_mc*|mc2|58.64|
+|**Total Average**|-|**71.56**||
+
+<br>
 
 ## Example Usage
 
@@ -20,3 +59,119 @@ USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
 ASSISTANT:
 ```
 
+### Below shows a code example on how to use this model:
+
+```python
+import torch, json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "migtissera/Synthia-70B-v1.2"
+output_file_path = "./Synthia-70B-conversations.jsonl"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    load_in_8bit=False,
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+
+def generate_text(instruction):
+    tokens = tokenizer.encode(instruction)
+    tokens = torch.LongTensor(tokens).unsqueeze(0)
+    tokens = tokens.to("cuda")
+
+    instance = {
+        "input_ids": tokens,
+        "top_p": 1.0,
+        "temperature": 0.75,
+        "generate_len": 1024,
+        "top_k": 50,
+    }
+
+    length = len(tokens[0])
+    with torch.no_grad():
+        rest = model.generate(
+            input_ids=tokens,
+            max_length=length + instance["generate_len"],
+            use_cache=True,
+            do_sample=True,
+            top_p=instance["top_p"],
+            temperature=instance["temperature"],
+            top_k=instance["top_k"],
+            num_return_sequences=1,
+        )
+    output = rest[0][length:]
+    string = tokenizer.decode(output, skip_special_tokens=True)
+    answer = string.split("USER:")[0].strip()
+    return f"{answer}"
+
+
+conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
+
+
+while True:
+    user_input = input("You: ")
+    llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
+    answer = generate_text(llm_prompt)
+    print(answer)
+    conversation = f"{llm_prompt}{answer}"
+    json_data = {"prompt": user_input, "answer": answer}
+
+    ## Save your conversation
+    with open(output_file_path, "a") as output_file:
+        output_file.write(json.dumps(json_data) + "\n")
+
+```
+
+<br>
+
+#### Limitations & Biases:
+
+While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. 
+
+Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. 
+
+Exercise caution and cross-check information when necessary. This is an uncensored model.
+
+
+<br>
+
+### Citiation:
+
+Please kindly cite using the following BibTeX:
+
+```
+@misc{Synthia-70B-v1.2,
+  author = {Migel Tissera},
+  title = {Synthia-70B-v1.2: Synthetic Intelligent Agent},
+  year = {2023},
+  publisher = {GitHub, HuggingFace},
+  journal = {GitHub repository, HuggingFace repository},
+  howpublished = {\url{https://huggingface.co/migtissera/Synthia-13B},
+}
+```
+
+```
+@misc{mukherjee2023orca,
+      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
+      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
+      year={2023},
+      eprint={2306.02707},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+```
+@software{touvron2023llama,
+  title={LLaMA2: Open and Efficient Foundation Language Models},
+  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
+  journal={arXiv preprint arXiv:2302.13971},
+  year={2023}
+}
+```
+