Update README.md

README.md

@@ -15,11 +15,10 @@ pipeline_tag: text-generation
 
 # Gemma-2 2B Instruct fine-tuned on JSON dataset
 
-This model is a Gemma-2 2b model finetuned on the paraloq/json_data_extraction.
+This model is a Gemma-2 2b model fine-tuned to paraloq/json_data_extraction.
 
-The model was finetuned in order to extract data from a text according to a json schema.
-
-# Prompt
+The model has been fine-tuned to extract data from a text according to a json schema.
+## Prompt
 
 The prompt used during training is:
 ```py
@@ -35,6 +34,44 @@ The prompt used during training is:
 """
 ```
 
+## Using the Model
+
+You can use the model with the transformer library or with the wrapper from [unsloth] (https://unsloth.ai/blog/gemma2), which allows faster inference.
+
+```py
+import torch
+from unsloth import FastLanguageModel
+
+# Required to avoid cache size exceeded
+torch._dynamo.config.accumulated_cache_size_limit = 2048
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = f"bastienp/Gemma-2-2B-it-JSON-data-extration",
+    max_seq_length = 2048,
+    dtype = torch.float16,
+    load_in_4bit = False,
+    token = HF_TOKEN_READ,
+)
+```
+
+## Using the Quantized model (llama.cpp)
+
+The model is supplied in GGFU format in 4bit and 8bit.
+
+Example code with Llamacpp:
+```py
+from llama_cpp import Llama
+
+llm = Llama.from_pretrained(
+    "bastienp/Gemma-2-2B-it-JSON-data-extration",
+    filename="*Q4_K_M.gguf", #*Q8_K_M.gguf for the 8 bit version
+    verbose=False,
+)
+```
+
+Thanks to the google team that provided gemma-2, this model follows the gemma licence, please check it out if you want to use this repository.
+
+
 - **Developed by:** bastienp
 - **License:** gemma
 - **Finetuned from model :** unsloth/gemma-2-2b-it