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Mwanzau
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Model Details
Model Provider
Mwanzau
Model Tree
unsloth/Llama-3.2-1B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Mwanzau/Mazgu_Llama-1B-V2-Knowledge-16bit" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") prompt = "### Instruction:\nLongosolani vya Yesu mu Mateyo 24.\n\n### Response:\n"inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.6, repetition_penalty=1.2) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) --- ### Option 2: Optimized 4-Bit Loading with Unsloth (Colab / Kaggle) from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "Mwanzau/Mazgu_Llama-1B-V2-Knowledge-16bit", max_seq_length = 2048, load_in_4bit = True, # Saves VRAM on T4 GPUs) FastLanguageModel.for_inference(model) prompt = "### Instruction:\nLongosolani vyakurya ivyo vili bwino ku munda.\n\n### Response:\n"inputs = tokenizer([prompt], return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.6, repetition_penalty=1.2)print(tokenizer.decode(outputs[0], skip_special_tokens=True)) --- ### 📊 SummaryFormat: Unsharded Native safetensorsPrecision: 16-bit Float (bfloat16/float16)Vocabulary Focus: Tumbuka instruction-following and factual knowledge retrieval.import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Mwanzau/Mazgu_Llama-1B-V2-Knowledge-16bit" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") prompt = "### Instruction:\nLongosolani vya Yesu mu Mateyo 24.\n\n### Response:\n"inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.6, repetition_penalty=1.2) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) --- ### Option 2: Optimized 4-Bit Loading with Unsloth (Colab / Kaggle) from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "Mwanzau/Mazgu_Llama-1B-V2-Knowledge-16bit", max_seq_length = 2048, load_in_4bit = True, # Saves VRAM on T4 GPUs) FastLanguageModel.for_inference(model) prompt = "### Instruction:\nLongosolani vyakurya ivyo vili bwino ku munda.\n\n### Response:\n"inputs = tokenizer([prompt], return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.6, repetition_penalty=1.2)print(tokenizer.decode(outputs[0], skip_special_tokens=True)) --- ### 📊 SummaryFormat: Unsharded Native safetensorsPrecision: 16-bit Float (bfloat16/float16)Vocabulary Focus: Tumbuka instruction-following and factual knowledge retrieval.