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Model: Mwanzau/Mazgu_Llama-1B-V2-Knowledge-16bit
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: unsloth/Llama-3.2-1B-Instruct
+tags:
+- safetensors
+- llama-3
+- merged
+- tumbuka
+- bantu
+- malawi
+language:
+- tum
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Mazgu_Llama-1B-V2-Knowledge-16bit (Standalone Model)
+
+**Mazgu_Llama-1B-V2-Knowledge-16bit** is a fully merged, standalone 16-bit `model.safetensors` release of Llama-3-1B, fine-tuned specifically for the Tumbuka language. 
+
+Because the LoRA weights are merged directly into the base architecture, this repository **does not require loading external adapter weights at runtime**. It serves as an ideal baseline for:
+* Direct inference via Hugging Face `transformers` or `vLLM`.
+* Continual pre-training or further SFT runs.
+* Full-model export and custom quantization pipelines.
+
+---
+
+## 💻 Quickstart Guide
+
+### Option 1: Standard Transformers
+
+```python
+import torch
+from 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))
+
+---
+
+### 📊 Summary
+Format: Unsharded Native safetensors
+Precision: 16-bit Float (bfloat16/float16)
+Vocabulary Focus: Tumbuka instruction-following and factual knowledge retrieval.
+
+