Update README.md

README.md

@@ -20,63 +20,67 @@ tags:
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 
-# How to Get Started with the Model
-Use the code below to get started with the model:
-
-Python Code:
-
-  %%capture
-  # Installs Unsloth, Xformers (Flash Attention) and all other packages!
-  !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
-  !pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes
-
-  from unsloth import FastLanguageModel
-  import torch
-  
-  # Define the dtype you want to use
-  dtype = torch.float16  # Example: using float16 for lower memory usage
-  
-  # Set load_in_4bit to True or False depending on your requirements
-  load_in_4bit = True  # Or False if you don't want to load in 4-bit
-  
-  # Verify the model name is correct and exists on Hugging Face Model Hub
-  model_name = "skkjodhpur/Meta-Llama-3.1-8B-Unsloth-2x-faster-finetuning-GGUF-by-skk" 
-  # Check if the model exists, if not, you may need to adjust the model name
-  !curl -s https://huggingface.co/{model_name}/resolve/main/config.json | jq .
-  
-  model, tokenizer = FastLanguageModel.from_pretrained(
-      model_name = model_name,
-      max_seq_length = 2048,
-      dtype = dtype,
-      load_in_4bit = load_in_4bit,
-  )
-  FastLanguageModel.for_inference(model) # Enable native 2x faster inference
-
-  # prompt = You MUST copy from above!
-
-  prompt = """Below is an tools that describes a task, paired with an query that provides further context. Write a answers that appropriately completes the request.
-  
-  ### tools:
-  {}
-  
-  ### query:
-  {}
-  
-  ### answers:
-  {}"""
-
-    inputs = tokenizer(
-  [
-      prompt.format(
-          '[{"name": "live_giveaways_by_type", "description": "Retrieve live giveaways from the GamerPower API based on the specified type.", "parameters": {"type": {"description": "The type of giveaways to retrieve (e.g., game, loot, beta).", "type": "str", "default": "game"}}}]', # instruction
-          "Where can I find live giveaways for beta access and games?", # input
-          "", # output - leave this blank for generation!
-      )
-  ], return_tensors = "pt").to("cuda")
-  
-  from transformers import TextStreamer
-  text_streamer = TextStreamer(tokenizer)
-  _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
+# Meta-Llama-3.1-8B-Unsloth-2x-faster-finetuning-GGUF-by-skk
+
+This repository contains the Meta-Llama-3.1-8B-Unsloth-2x-faster-finetuning-GGUF model, optimized for faster inference.
+
+## Getting Started
+
+Use the following Python code to get started with the model:
+
+```python
+%%capture
+# Installs Unsloth, Xformers (Flash Attention) and all other packages!
+!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+!pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes
+
+from unsloth import FastLanguageModel
+import torch
+
+# Define the dtype you want to use
+dtype = torch.float16  # Example: using float16 for lower memory usage
+
+# Set load_in_4bit to True or False depending on your requirements
+load_in_4bit = True  # Or False if you don't want to load in 4-bit
+
+# Verify the model name is correct and exists on Hugging Face Model Hub
+model_name = "skkjodhpur/Meta-Llama-3.1-8B-Unsloth-2x-faster-finetuning-GGUF-by-skk" 
+# Check if the model exists, if not, you may need to adjust the model name
+!curl -s https://huggingface.co/{model_name}/resolve/main/config.json | jq .
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = model_name,
+    max_seq_length = 2048,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# prompt = You MUST copy from above!
+
+prompt = """Below is an tools that describes a task, paired with an query that provides further context. Write a answers that appropriately completes the request.
+
+### tools:
+{}
+
+### query:
+{}
+
+### answers:
+{}"""
+
+inputs = tokenizer(
+[
+    prompt.format(
+        '[{"name": "live_giveaways_by_type", "description": "Retrieve live giveaways from the GamerPower API based on the specified type.", "parameters": {"type": {"description": "The type of giveaways to retrieve (e.g., game, loot, beta).", "type": "str", "default": "game"}}}]', # instruction
+        "Where can I find live giveaways for beta access and games?", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
 
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)