Update Readme

README.md

@@ -1,15 +1,21 @@
 ---
 base_model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit
+license: apache-2.0
+language:
+- en
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit
+- qwen
+- bitcoin
+- finance
+- text-generation
 - lora
-- transformers
 - unsloth
+- autotrain
+datasets:
+- tahamajs/not_clearned_bitcoin_dataset
 ---
-
-base_model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bitlicense: apache-2.0language:enlibrary_name: pefttags:qwenbitcoinfinancetext-generationloraunslothautotraindatasets:tahamajs/not_clearned_bitcoin_datasetModel Card for Qwen-Bitcoin-ChatThis model is a fine-tuned version of unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit, specialized for predicting Bitcoin prices based on contextual financial data. It has been trained to act as an expert financial analyst, processing historical prices, news, and market metrics to forecast the next 10 days of Bitcoin's closing price.Model DetailsModel DescriptionThis model is a powerful yet efficient language model fine-tuned using the Unsloth library for PEFT (Parameter-Efficient Fine-Tuning) with LoRA. It is designed to understand a complex prompt containing a system message, historical context, and real-time data points (like news headlines, social media sentiment, and other financial indicators).The model's primary function is to generate a structured response that includes:A brief analysis of the provided market data.A comma-separated list of 10 price predictions for the next 10 days.Special tokens (<|response|>, <|analysis|>, <|forecast|>) are used to structure the output, making it easy to parse for downstream applications.Developed by: tahamajsModel type: Causal Language Model based on the Qwen architecture.Language(s) (NLP): EnglishLicense: Apache 2.0Finetuned from model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bitModel SourcesRepository: https://huggingface.co/tahamajs/qwen-bitcoin-chat-manual-Qwen3-4B-Thinking-2507UsesDirect UseThis model is intended for generating Bitcoin price forecasts based on provided data. It can be used directly via the transformers library pipeline or by manually creating the prompt. The model expects a specific chat format, as shown in the example below.Important: This model is for informational and research purposes only. It is not financial advice. Cryptocurrency markets are highly volatile, and predictions should not be used for making investment decisions without consulting a qualified financial advisor.Downstream UseThis model can be integrated into various applications, such as:Financial Dashboards: To provide automated daily market commentary and price outlooks.Trading Bots: As a signal generator, where its analysis and forecasts can be one of several inputs for an automated trading strategy.Research Tools: To study the relationship between news sentiment, market data, and price movements.Out-of-Scope UseDo not use this model as a sole source for financial decisions. Its predictions are based on patterns in the training data and are not guaranteed to be accurate.The model is not designed for general conversation and may perform poorly on tasks outside of financial analysis and prediction.The model should not be used to generate misleading or harmful financial content.Bias, Risks, and LimitationsData Bias: The model's predictions are heavily influenced by the historical data it was trained on (tahamajs/not_clearned_bitcoin_dataset). It may not perform well in unprecedented market conditions or "black swan" events.No Real-time Knowledge: The model does not have access to real-time information beyond what is provided in the prompt. Its predictions are only as good as the data it receives.Hallucination: Like all language models, it can generate information that is plausible-sounding but factually incorrect. The "Analysis" section should be fact-checked if used for any serious purpose.Volatility Risk: The cryptocurrency market is extremely volatile. The model cannot predict sudden market crashes or spikes caused by unforeseen global events.RecommendationsUsers should treat the model's output as a supplementary tool, not as a definitive forecast. Always combine its analysis with other research and professional advice before making any financial commitments.How to Get Started with the ModelUse the code below to load the model and run inference. Make sure you have the necessary libraries installed (transformers, torch, accelerate, bitsandbytes, peft).import torch
+Model Card for Qwen-Bitcoin-ChatThis model is a fine-tuned version of unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit, specialized for predicting Bitcoin prices based on contextual financial data. It has been trained to act as an expert financial analyst, processing historical prices, news, and market metrics to forecast the next 10 days of Bitcoin's closing price.Model DetailsModel DescriptionThis model is a powerful yet efficient language model fine-tuned using the Unsloth library for PEFT (Parameter-Efficient Fine-Tuning) with LoRA. It is designed to understand a complex prompt containing a system message, historical context, and real-time data points (like news headlines, social media sentiment, and other financial indicators).The model's primary function is to generate a structured response that includes:A brief analysis of the provided market data.A comma-separated list of 10 price predictions for the next 10 days.Special tokens (<|response|>, <|analysis|>, <|forecast|>) are used to structure the output, making it easy to parse for downstream applications.Developed by: tahamajsModel type: Causal Language Model based on the Qwen architecture.Language(s) (NLP): EnglishLicense: Apache 2.0Finetuned from model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bitModel SourcesRepository: https://huggingface.co/tahamajs/qwen-bitcoin-chat-manual-Qwen3-4B-Thinking-2507UsesDirect UseThis model is intended for generating Bitcoin price forecasts based on provided data. It can be used directly via the transformers library pipeline or by manually creating the prompt. The model expects a specific chat format, as shown in the example below.Important: This model is for informational and research purposes only. It is not financial advice. Cryptocurrency markets are highly volatile, and predictions should not be used for making investment decisions without consulting a qualified financial advisor.Downstream UseThis model can be integrated into various applications, such as:Financial Dashboards: To provide automated daily market commentary and price outlooks.Trading Bots: As a signal generator, where its analysis and forecasts can be one of several inputs for an automated trading strategy.Research Tools: To study the relationship between news sentiment, market data, and price movements.Out-of-Scope UseDo not use this model as a sole source for financial decisions. Its predictions are based on patterns in the training data and are not guaranteed to be accurate.The model is not designed for general conversation and may perform poorly on tasks outside of financial analysis and prediction.The model should not be used to generate misleading or harmful financial content.Bias, Risks, and LimitationsData Bias: The model's predictions are heavily influenced by the historical data it was trained on (tahamajs/not_clearned_bitcoin_dataset). It may not perform well in unprecedented market conditions or "black swan" events.No Real-time Knowledge: The model does not have access to real-time information beyond what is provided in the prompt. Its predictions are only as good as the data it receives.Hallucination: Like all language models, it can generate information that is plausible-sounding but factually incorrect. The "Analysis" section should be fact-checked if used for any serious purpose.Volatility Risk: The cryptocurrency market is extremely volatile. The model cannot predict sudden market crashes or spikes caused by unforeseen global events.RecommendationsUsers should treat the model's output as a supplementary tool, not as a definitive forecast. Always combine its analysis with other research and professional advice before making any financial commitments.How to Get Started with the ModelUse the code below to load the model and run inference. Make sure you have the necessary libraries installed (transformers, torch, accelerate, bitsandbytes, peft).import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_id = "tahamajs/qwen-bitcoin-chat-manual-Qwen3-4B-Thinking-2507"
@@ -78,8 +84,7 @@ if response_start != -1:
     response_content = decoded_output[response_start:]
     print("\n--- Parsed Response ---")
     print(response_content)
-
 Training DetailsTraining DataThe model was fine-tuned on the tahamajs/not_clearned_bitcoin_dataset dataset. This dataset contains historical Bitcoin data, including price, volume, news headlines, and other market indicators. Each entry was formatted into a conversational prompt structure to teach the model to act as a financial analyst.Training ProcedureThe model was trained using the SFTTrainer from the TRL library, which is optimized for instruction fine-tuning.PreprocessingThe raw data was transformed into a chat format using a custom function (format_chat). This function structures the input with system, user, and assistant roles. Special tokens (<|response|>, <|analysis|>, <|forecast|>) were added to the tokenizer to delineate the different parts of the model's expected output.A custom DataCollatorMaskResponse was used to ensure that the model is only trained to predict the assistant's response, masking out the user and system prompts from the loss calculation. This focuses the learning process on generating the desired analysis and forecast.Training HyperparametersFramework: PyTorchLibrary: Unsloth for PEFT (LoRA)Base Model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bitLoRA Rank (r): 32LoRA Alpha (lora_alpha): 32Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_projEpochs: 4Max Steps: 400Per Device Train Batch Size: 4Gradient Accumulation Steps: 1Optimizer: AdamW (adamw_torch)Learning Rate: 2e-4LR Scheduler: CosineWarmup Ratio: 0.05Precision: fp16Framework versionsTransformers 4.41.2Pytorch 2.3.0+cu121Datasets 2.19.1PEFT 0.11.1Unsloth 2024.5
 
-By Taha Majlesi
-- PEFT 0.17.0
+
+By taha majlesi