bpavlsh
/

Mistral-crypto-news

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 ---
 base_model: mistralai/Mistral-7B-Instruct-v0.1
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1
-- lora
-- sft
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-- trl
 ---
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-### Framework versions
-- PEFT 0.17.0

 ---
 base_model: mistralai/Mistral-7B-Instruct-v0.1
 library_name: peft
 tags:
+  - crypto news
+  - news analytics
+  - graph summary
+  - sentiment signals
+  - RAG
+  - peft
+  - summarization
+license: apache-2.0
 ---
+ ## Model Description
+ Fine-tuned Mistral-7B model for cryptocurrency news multilevel analytics (English language).
+It was fine-tuned using Peft/LoRA approach with 4-Bit quantization. Given the news text, the model can generate graph
+and text summaries with sentiment signals. Graph and text summaries and be summarized on the stacking level and graph and text  summaries of summaries
+can be stacked into one diversified summary. Using graph presentation can be improve trend estimation accuracy for cryptocurrencies and eliminate LLM halucinations.
+ ## How to Get Started with the Model
+ Fine-tuned model can be tested on Google Colab using Nvidia A100 or L4 GPU.
+Pakages installation:
+```python
+pip install transformers bitsandbytes peft
+```
+Use the code below to get started with the model:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+from huggingface_hub import login
+#Login to Huggingface to load Mistral LLM
+login("Huggingface access token")
+model_id = "mistralai/Mistral-7B-Instruct-v0.1"
+peft_model_name="bpavlsh/Mistral-crypto-news"
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
+base_model = AutoModelForCausalLM.from_pretrained( model_id, load_in_4bit=True,
+                                  device_map="auto", torch_dtype="auto")
+model = PeftModel.from_pretrained(base_model, peft_model_name)
+ text=""" News text for analysis, from 1Kb to 10Kb """
+ prompt = f"""<s>[INST] <<SYS>>
+You are an expert in analyzing news for fake content, propaganda, and offensive language.
+<</SYS>>
+ Please analyze the following text: {text} [/INST]"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=1500)
+output_result=tokenizer.decode(output[0], skip_special_tokens=True)
+result=output_result.split('[/INST]')[1]
+print(f"\n{result}")
+```
+ ## References
+ Pavlyshenko B.M. Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model. arXiv preprint arXiv:2309.04704. 2023. Download PDF: https://arxiv.org/pdf/2309.04704.pdf
+ Pavlyshenko B.M. Financial News Analytics Using Fine-Tuned Llama 2 GPT Model. arXiv preprint arXiv:2308.13032. 2023. Download PDF: https://arxiv.org/pdf/2308.13032.pdf
+ Pavlyshenko B.M. AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity. arXiv preprint arXiv:2505.06313. 2025. Download PDF: https://arxiv.org/pdf/2505.06313
+ ## Disclaimer
+ We are sharing a considered model and results for academic purpose only,
+not any  advice or recommendations.
+ ## Contacts
+ B. Pavlyshenko https://www.linkedin.com/in/bpavlyshenko