Update README.md

README.md

@@ -16,17 +16,24 @@ language:
 
 # Model Card for Llama-3.2-1B-Instruct-APIGen-FC-v0.1
 
-This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [argilla-warehouse/apigen-synth-trl](https://huggingface.co/datasets/plaguss/apigen-synth-trl) dataset.
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on
+the [argilla-warehouse/apigen-synth-trl](https://huggingface.co/datasets/plaguss/apigen-synth-trl) dataset, a version of 
+[argilla-warehouse/Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla-warehouse/Synth-APIGen-v0.1) ready to do SFT.
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
 
-See different examples of using the model:
+This is a Fine tuned version of `Llama-3.2-1B-Instruct` model specific for Function Calling, to showcase how to fine tune a model on top of a dataset
+like [argilla-warehouse/Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla-warehouse/Synth-APIGen-v0.1). This dataset can be merged with the original
+[Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) and prepared with any custom format.
 
-<details><summary> Click to see prepare_messages function </summary>
+The following examples show how to use the model with transformers, for different types of queries and availability of tools:
+
+<details><summary> Click to see helper functions </summary>
 
 ````python
 from typing import Optional
+import re
 import json
 
 from jinja2 import Template
@@ -82,17 +89,7 @@ def prepare_messages(
         ]
 
     return messages
-    
-````
 
-</details>
-
-
-<details><summary> Click to see parse_response function </summary>
-
-```python
-import re
-import json
 
 def parse_response(text: str) -> str | dict[str, any]:
     """Parses a response from the model, returning either the
@@ -107,10 +104,8 @@ def parse_response(text: str) -> str | dict[str, any]:
     if matches:
         return json.loads(matches[0])
     return text
-
-```
-
-</details>
+    
+````
 
 
 Example of *simple* function call:
@@ -119,7 +114,7 @@ Example of *simple* function call:
 import torch 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "plaguss/Llama-3.2-1B-Instruct-APIGen-FC-v0.1"
+model_name = "argilla-warehouse/Llama-3.2-1B-Instruct-APIGen-FC-v0.1"
 model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_name)