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paulaschez
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Model Details
Model Provider
paulaschez
Model Tree
mistralai/Mistral-7B-v0.1
Input Modalities
Output Modalities
Supported Functionality
| Field | Value |
|---|---|
| Base model | mistralai/Mistral-7B-v0.1 |
| Fine-tuning technique | QLoRA (4-bit NF4 + LoRA) |
| LoRA rank (r) | 16 |
| LoRA alpha | 16 |
| Target modules | q, k, v, o, gate, up, down proj |
| Trainable parameters | 41,943,040 (0.58% of total) |
| Training examples | 3,000 |
| Epochs | 1 |
| Final eval loss | 0.454 |
| Training tool | Unsloth on Google Colab T4 |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport torch # Load base model + adaptertokenizer = AutoTokenizer.from_pretrained("paulaschez/Mistral-7B-AI-Chef")model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, "paulaschez/Mistral-7B-AI-Chef") # Example promptprompt = """[INST] You are AI Chef, an advanced culinary and nutrition assistant \for SmartKitchen Solutions. Given a list of available ingredients and dietary \restrictions, generate a complete, structured recipe with nutritional information. Ingredients: eggs, tomato, onion, olive oilDietary Restrictions: no specific restrictions [/INST]""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=400, temperature=0.3)print(tokenizer.decode(outputs[0], skip_special_tokens=True))from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport torch # Load base model + adaptertokenizer = AutoTokenizer.from_pretrained("paulaschez/Mistral-7B-AI-Chef")model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, "paulaschez/Mistral-7B-AI-Chef") # Example promptprompt = """[INST] You are AI Chef, an advanced culinary and nutrition assistant \for SmartKitchen Solutions. Given a list of available ingredients and dietary \restrictions, generate a complete, structured recipe with nutritional information. Ingredients: eggs, tomato, onion, olive oilDietary Restrictions: no specific restrictions [/INST]""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=400, temperature=0.3)print(tokenizer.decode(outputs[0], skip_special_tokens=True))