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Rudraksh225
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Model Details
Model Provider
Rudraksh225
Model Tree
unsloth/Qwen2.5-7B-Instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
trl SFTTrainerqwen-2.5)The model is fine-tuned to act as a compassionate, balanced, non-fatalistic Vedic astrology assistant. It is trained to:
python
from unsloth import FastLanguageModelfrom unsloth.chat_templates import get_chat_template model, tokenizer = FastLanguageModel.from_pretrained( model_name="Rudraksh225/qwen2.5-7b-vedaz-astrologer-lora", max_seq_length=2048, dtype=None, load_in_4bit=True,)FastLanguageModel.for_inference(model) tokenizer = get_chat_template(tokenizer, chat_template="qwen-2.5") messages = [ {"role": "system", "content": "आप Vedaz के AI ज्योतिषी हैं। आप करुणामय, संतुलित और गैर-भाग्यवादी मार्गदर्शन देते हैं। आप कभी मृत्यु या किसी अनहोनी की भविष्यवाणी नहीं करते।"}, {"role": "user", "content": "Mera career kaisa rahega is saal?"},] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(input_ids=inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model = AutoModelForCausalLM.from_pretrained( "unsloth/Qwen2.5-7B-Instruct-bnb-4bit", device_map="auto", torch_dtype=torch.bfloat16,)model = PeftModel.from_pretrained(base_model, "Rudraksh225/qwen2.5-7b-vedaz-astrologer-lora")tokenizer = AutoTokenizer.from_pretrained("Rudraksh225/qwen2.5-7b-vedaz-astrologer-lora")Fine-tuned on a custom dataset of ~55 conversations (system + user + assistant turns) covering Vedic astrology Q&A in Hindi/Hinglish, including examples of safe handling of sensitive/crisis user messages.
trl (SFTTrainer / SFTConfig)This adapter inherits licensing terms from the base model (unsloth/Qwen2.5-7B-Instruct-bnb-4bit / Qwen2.5 license). Please review the base model's license before commercial use.
from unsloth import FastLanguageModelfrom unsloth.chat_templates import get_chat_template model, tokenizer = FastLanguageModel.from_pretrained( model_name="Rudraksh225/qwen2.5-7b-vedaz-astrologer-lora", max_seq_length=2048, dtype=None, load_in_4bit=True,)FastLanguageModel.for_inference(model) tokenizer = get_chat_template(tokenizer, chat_template="qwen-2.5") messages = [ {"role": "system", "content": "आप Vedaz के AI ज्योतिषी हैं। आप करुणामय, संतुलित और गैर-भाग्यवादी मार्गदर्शन देते हैं। आप कभी मृत्यु या किसी अनहोनी की भविष्यवाणी नहीं करते।"}, {"role": "user", "content": "Mera career kaisa rahega is saal?"},] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(input_ids=inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model = AutoModelForCausalLM.from_pretrained( "unsloth/Qwen2.5-7B-Instruct-bnb-4bit", device_map="auto", torch_dtype=torch.bfloat16,)model = PeftModel.from_pretrained(base_model, "Rudraksh225/qwen2.5-7b-vedaz-astrologer-lora")tokenizer = AutoTokenizer.from_pretrained("Rudraksh225/qwen2.5-7b-vedaz-astrologer-lora")