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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
pipeline_tag: text-generation
---
# 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 |