Update README.md

README.md

@@ -9,26 +9,22 @@ language:
 - en
 ---
 
-# LLama3-Gaja-Hindi-8B-v0.1
+# Eli
 
 ## Overview
 
-LLama3-Gaja-Hindi-8B-v0.1 is an extension of the Ambari series, a bilingual English/Hindi model developed and released by [Cognitivelab.in](https://www.cognitivelab.in/). This model is specialized for natural language understanding tasks, particularly in the context of instructional pairs. It is built upon the [Llama3 8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B) model, utilizing a fine-tuning process with a curated dataset of translated instructional pairs.
-
-<img src="https://cdn-uploads.huggingface.co/production/uploads/6442d975ad54813badc1ddf7/G0u9L6RQJFinST0chQmfL.jpeg" width="500px">
-
 ## Generate
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers import GenerationConfig, TextStreamer , TextIteratorStreamer
 
-model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", torch_dtype=torch.bfloat16).to("cuda")
-tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Neohumans-ai/Eli", torch_dtype=torch.bfloat16).to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("Neohumans-ai/Eli", trust_remote_code=True)
 
 # Existing messages list
 messages = [
-    {"role": "system", "content": " You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
+    {"role": "system", "content": " You are Eli, an AI assistant created by NeoHumans-ai and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
     {"role": "user", "content": "Who are you"}
 ]
 
@@ -54,19 +50,19 @@ print(tokenizer.decode(response, skip_special_tokens=True))
 
 ## Multi-turn Chat
 
-To use the Ambari-7B-Instruct-v0.1 model, you can follow the example code below:
+To use the Eli model, you can follow the example code below:
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers import GenerationConfig, TextStreamer , TextIteratorStreamer
 
-model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", torch_dtype=torch.bfloat16).to("cuda")
-tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Neohumans-ai/Eli", torch_dtype=torch.bfloat16).to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("Neohumans-ai/Eli", trust_remote_code=True)
 
 # Existing messages list
 messages = [
-    {"role": "system", "content": " You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
+    {"role": "system", "content": " You are Eli, an AI assistant created by NeoHumans-ai and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
 ]
 
 # Function to add user input and generate response
@@ -147,7 +143,7 @@ while True:
 
 ## Prompt formate
 
-system prompt = `You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model(LLM), proficient in English and Hindi. You can respond in both languages based on the users request.`
+system prompt = `You are Eli, an AI assistant created by NeoHumans-ai and trained on top of Llama 3 Large language model(LLM), proficient in English and Hindi. You can respond in both languages based on the users request.`
 ```
 <|begin_of_text|><|start_header_id|>system<|end_header_id|>
 
@@ -162,11 +158,3 @@ system prompt = `You are Gaja, an AI assistant created by Cognitivelab and train
 
 ## Benchmarks 
 coming soon
-
-## Bilingual Instruct Fine-tuning
-
-The model underwent a pivotal stage of supervised fine-tuning with low-rank adaptation, focusing on bilingual instruct fine-tuning. This approach involved training the model to respond adeptly in either English or Hindi based on the language specified in the user prompt or instruction.
-
-## References
-
-- [Ambari-7B-Instruct Model](https://huggingface.co/Cognitive-Lab/Ambari-7B-Instruct-v0.1)