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nassimjp
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nassimjp
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This model has been quantized down to 4-bit precision, significantly reducing VRAM requirements (running efficiently under ~6 GB VRAM), making it ideal for consumer GPUs and resource-constrained deployment environments without sacrificing its fundamental reasoning and structural integrity.
You can load and use this model easily with the Hugging Face transformers and bitsandbytes libraries:
python
import torchfrom transformers import AutoTokenizer, Mistral3ForConditionalGeneration MODEL_ID = "nassimjp/Ministral-3-8B-Reasoning-4bit" print("Loading tokenizer and 4-bit model...")tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) model = Mistral3ForConditionalGeneration.from_pretrained( MODEL_ID, device_map="auto", trust_remote_code=True) # Example promptmessages = [ { "role": "user", "content": "Hello! Can you briefly explain what reasoning models are?" }] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=300, do_sample=True, temperature=0.7, top_p=0.9,) response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)model-00001-of-00002.safetensors (3.98 GB)model-00002-of-00002.safetensors (2.22 GB)config.json & generation_config.jsontokenizer.json & chat_template.jinjaOptimized and published by nassimjp.
import torchfrom transformers import AutoTokenizer, Mistral3ForConditionalGeneration MODEL_ID = "nassimjp/Ministral-3-8B-Reasoning-4bit" print("Loading tokenizer and 4-bit model...")tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) model = Mistral3ForConditionalGeneration.from_pretrained( MODEL_ID, device_map="auto", trust_remote_code=True) # Example promptmessages = [ { "role": "user", "content": "Hello! Can you briefly explain what reasoning models are?" }] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=300, do_sample=True, temperature=0.7, top_p=0.9,) response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)