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Model: bastienp/Gemma-2-2B-Instruct-structured-output
Source: Original Platform

README.md

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+---
+base_model: google/gemma-2-2b-it
+language:
+- en
+license: gemma
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- gemma2
+datasets:
+- paraloq/json_data_extraction
+library_name: peft
+---
+
+# Gemma-2 2B Instruct fine-tuned on JSON dataset
+
+This model is a Gemma-2 2b model fine-tuned to paraloq/json_data_extraction.
+
+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
+"""Below is a text paired with input that provides further context. Write JSON output that matches the schema to extract information.
+
+### Input:
+{input}
+
+### Schema:
+{schema}
+
+### Response:
+"""
+```
+
+## 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,
+)
+```
+
+The base model used for fine-tuning is google/gemma-2-2b-it. This repository is **NOT** affiliated with Google.
+
+Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms. 
+
+- **Developed by:** bastienp
+- **License:** gemma
+- **Finetuned from model :** google/gemma-2-2b-it