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RRK1987
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Model Details
Model Provider
RRK1987
Model Tree
mistralai/Mistral-7B-Instruct-v0.2
Input Modalities
Output Modalities
Supported Functionality
This model is a fine-tuned version of Mistral-7B-Instruct-v0.2, specialised for automotive vehicle diagnostics using OBD-II and UDS fault code protocols.
The model acts as an expert automotive diagnostics assistant that:
| Parameter | Value |
|---|---|
| Base model | Mistral-7B-Instruct-v0.2 |
| Technique | QLoRA — 4-bit NF4 quantization |
| LoRA rank (r) | 16 |
| LoRA alpha | 32 |
| Training examples | 119 |
| Validation examples | 21 |
| Epochs | 3 |
| Final loss | 0.4590 |
| Optimizer | paged_adamw_8bit |
| Precision | bf16 |
| Hardware | Tesla T4 GPU (15.6GB) |
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch base_model = "mistralai/Mistral-7B-Instruct-v0.2"peft_model = "RRK1987/automotive-diagnostics-llm" tokenizer = AutoTokenizer.from_pretrained(peft_model)model = AutoModelForCausalLM.from_pretrained( base_model, load_in_4bit=True, device_map="auto",)model = PeftModel.from_pretrained(model, peft_model) prompt = "<s>[INST] You are an expert automotive diagnostics assistant. My 2019 Toyota Camry is showing fault code P0300. I am experiencing rough idle and engine shaking. Can you diagnose this? [/INST]"inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=300)print(tokenizer.decode(outputs[0], skip_special_tokens=True))This model includes safety guardrails that refuse requests to:
This model is for educational purposes only. Always consult a certified mechanic for actual vehicle repairs, especially for safety-critical systems.
Training data available at: https://huggingface.co/datasets/RRK1987/automotive-dtc-finetune
This model is part of a 5-phase GenAI capstone project:
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch base_model = "mistralai/Mistral-7B-Instruct-v0.2"peft_model = "RRK1987/automotive-diagnostics-llm" tokenizer = AutoTokenizer.from_pretrained(peft_model)model = AutoModelForCausalLM.from_pretrained( base_model, load_in_4bit=True, device_map="auto",)model = PeftModel.from_pretrained(model, peft_model) prompt = "<s>[INST] You are an expert automotive diagnostics assistant. My 2019 Toyota Camry is showing fault code P0300. I am experiencing rough idle and engine shaking. Can you diagnose this? [/INST]"inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=300)print(tokenizer.decode(outputs[0], skip_special_tokens=True))