Update README.md

README.md

@@ -3,9 +3,15 @@ base_model: Alignment-Lab-AI/Neural-network-medium-untuned-theta
 tags:
 - axolotl
 - Alignment-Lab-AI
+- Meta-Llama-3
 model-index:
-- name: Buzz-5B-Medium
+- name: Buzz-8b-Large-0.5
   results: []
+license: apache-2.0
+datasets:
+- H-D-T/Buzz
+language:
+- en
 ---
 [<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)
 
@@ -13,25 +19,26 @@ model-index:
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6436279eaaef013d1af225c9/fWaQucBWfabfnMsAFN8hv.png)
 
-# Buzz-5b-Medium: Advancing Efficiency through Iterative Fine-Tuning
+# Buzz-8b-Large: Advancing Efficiency through Iterative Fine-Tuning
 
 ## Introduction
 
 - [Alignment Lab AI](https://AlignmentLab.ai) is pleased to introduce our latest research efforts with: 
 
-**Buzz-5b-Medium**, a state-of-the-art language model developed in collaboration with [Hive Digital Technologies](https://hivedt.com/). 
+**Buzz-8b-Large**, a state-of-the-art language model developed in collaboration with [Hive Digital Technologies](https://hivedt.com/). 
 
 The Buzz model, Dataset, and Code are to be released to build a toolkit that aims to demonstrate the potential for reuse and optimization of existing pretrained language models to continuously refine the heights of performance that can be achieved with optimal use of FlOps. Alongside Buzz-5b-Medium, we release 
 
-- [The Buzz Dataset](https://huggingface.co/datasets/tempbuzz/Buzz)
-- [Buzz-2.5b-Small](https://huggingface.co/tempbuzz/buzz-Buzz-2.5b-Small)
+- [The Buzz Dataset](https://huggingface.co/datasets/H-D-T/Buzz)
+- [Buzz-2.5b-Small] soon!
+- [Buzz-5b-Medium] soon!
 - [Buzz-8B-Large](https://huggingface.co/tempbuzz/Lab-AI/Buzz-8B-Large)
 
-the **Buzz dataset** and two additional models: **Buzz-2.5B-Small** (2.5B parameters) and **Buzz-8B-Large** (8B parameters), the codebase to refine, filter and augment the data, as well as prune and train your own variants, will additionally be released in the coming days.
+the **Buzz dataset** and two additional models: **Buzz-2.5B-Small** and **Buzz-5B-Medium**, the codebase to refine, filter and augment the data, as well as prune and train your own variants, will additionally be released in the coming days.
 
 ## Performance
 
-Buzz-5b-Medium achieves remarkably low train and validation loss, with unseen data loss reaching around **0.5** by the end of training. This performance showcases the effectiveness of our novel iterative fine-tuning approach, which maximizes the reuse of pretrained weights. Even the smallest variant, Buzz-Small, maintains a steady train loss of approximately **0.4-0.6**, on entirely new data and hold out sets.
+Buzz-8b-Large achieves remarkably low train and validation loss, with unseen data loss reaching around **0.5** by the end of training. This performance showcases the effectiveness of our novel iterative fine-tuning approach, which maximizes the reuse of pretrained weights. Even the smallest variant, Buzz-Small, maintains a steady train loss of approximately **0.4-0.6**, on entirely new data and hold out sets.
 
 [ benchmark scores table here]
 
@@ -51,37 +58,42 @@ By combining high quality data, iterative fine-tuning with carefully selected "g
 
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6436279eaaef013d1af225c9/wyHyDIJnNmbomonZKQAD0.png)
-https://wandb.ai/llm_surgery/llama-3-8b-vs-5b
-https://wandb.ai/autometa/neural-network-1
-https://wandb.ai/autometa/buzz-baby?nw=nwuserautometa
-https://wandb.ai/autometa/buzz-brother?nw=nwuserautometa
-https://wandb.ai/autometa/buzz-big?nw=nwuserautometa
 
 ## Chat Template and Inference
 
-To use the Buzz-5b-Medium model for chat-based tasks, you can utilize the provided chat template. Here's an example of how to format the chat template and perform inference using the Hugging Face Transformers library:
+To use the Buzz-8b-Medium model for chat-based tasks, you can utilize the provided chat template. Here's an example of how to format the chat template and perform inference using the Hugging Face Transformers library:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-model_name = "tempbuzz/Buzz-5b-Medium"
+# Load the tokenizer and model
+model_name = "H-D-T/Buzz-8b-Large-v0.5"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-chat_template = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"""
+# Set the device to run the model on (e.g., "cuda" for GPU, "cpu" for CPU)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
 
-messages = [
-    {"role": "user", "content": "Hello, how are you?"},
-    {"role": "assistant", "content": "I'm doing well, thank you for asking! How can I assist you today?"},
-    {"role": "user", "content": "Can you tell me a joke?"}
-]
+# Define the input prompt
+prompt = "Hello, how are you today?"
 
-input_text = chat_template.format(messages=messages, add_generation_prompt=True)
-input_ids = tokenizer.encode(input_text, return_tensors="pt")
+# Tokenize the input prompt
+input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
 
-output = model.generate(input_ids, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
-generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+# Generate the model's response
+output = model.generate(
+    input_ids,
+    max_length=100,
+    num_return_sequences=1,
+    no_repeat_ngram_size=2,
+    early_stopping=True
+)
 
-print(generated_text)
+# Decode the generated response
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print("Input:", prompt)
+print("Response:", response)
 ``````
 ## Conclusion
 
@@ -152,4 +164,4 @@ as well as many, many others who are too numerous to name.
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
+```