Update README.md

README.md

@@ -1,3 +1,68 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- motexture/cData
+language:
+- en
+- it
+- es
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+tags:
+- smoll
+- coding
+- coder
+- model
+- small
+---
+
+# LlamaXCoder-3.2-3B-Instruct
+
+## Introduction
+
+LlamaXCoder-3.2-3B-Instruct is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct, trained on the cData coding dataset to improve its reasoning and coding ability.
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "motexture/LlamaXCoder-3.2-3B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("motexture/LlamaXCoder-3.2-3B-Instruct")
+
+prompt = "Write a C++ program that prints Hello World!"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+        model_inputs.input_ids,
+        max_new_tokens=4096,
+        do_sample=True,
+        temperature=0.3
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## License
+
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)