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README.md

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+---
+tags:
+- autotrain
+- text-generation
+- mistral
+- fine-tune
+- text-generation-inference
+- chat
+- Trained with Auto-train
+- pytorch
+widget:
+- text: 'I love AutoTrain because '
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: conversational
+---
+
+# Model Trained Using AutoTrain
+
+![LLM_IMAGE](header.jpeg)
+
+The mistral-7b-fraud2-finetuned Large Language Model (LLM) is a fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of synthetically generated Fraudulent transcripts datasets.
+
+For full details of this model please read [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
+
+## Instruction format
+
+In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
+
+E.g.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
+
+text = "<s>[INST] Below is a conversation transcript [/INST]"
+"Your credit card has been stolen, and you need to contact us to resolve the issue. We will help you protect your information and prevent further fraud.</s> "
+"[INST] Analyze the conversation and determine if it's fraudulent or legitimate. [/INST]"
+
+encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False)
+
+model_inputs = encodeds.to(device)
+model.to(device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])
+```
+
+## Model Architecture
+This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
+- Grouped-Query Attention
+- Sliding-Window Attention
+- Byte-fallback BPE tokenizer
+
+## Version
+- v1
+
+## The  Team
+- BILIC TEAM OF AI ENGINEERS

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