---

license: llama3.2

base_model: Vikhrmodels/Vikhr-Llama-3.2-1B-Instruct

tags:

- llama-cpp

- gguf

- llama-3.2

- russian

- sft

- quantized

language:

- ru

- en

pipeline_tag: text-generation

library_name: gguf

---

Vikhr-Llama-3.2-1B-Instruct GGUF

GGUF quantizations of Vikhrmodels/Vikhr-Llama-3.2-1B-Instruct.

Quantization Table

| File | Bits | Size | Use Case |

|------|------|------|----------|

| model_f16.gguf | 16 | ~2.5 GB | Maximum quality, reference |

| model_q8_0.gguf | 8 | ~1.3 GB | Best quality, near-lossless |

| model_q6_k.gguf | 6 | ~1.0 GB | Great quality |

| model_q5_k_m.gguf | 5 | ~0.9 GB | Balanced quality/size |

| model_q5_k_s.gguf | 5 | ~0.85 GB | Slightly smaller Q5 |

| model_q4_k_m.gguf | 4 | ~0.77 GB | Recommended, good balance |

| model_q4_k_s.gguf | 4 | ~0.72 GB | Smaller Q4 |

| model_q3_k_l.gguf | 3 | ~0.65 GB | Low VRAM, decent quality |

| model_q3_k_m.gguf | 3 | ~0.60 GB | Low VRAM |

| model_q3_k_s.gguf | 3 | ~0.55 GB | Very low VRAM |

| model_q2_k.gguf | 2 | ~0.45 GB | Minimum size, lowest quality |

VRAM Requirements

| Quant | VRAM |

|-------|------|

| F16 | ~3 GB |

| Q8_0 | ~1.8 GB |

| Q6_K | ~1.5 GB |

| Q5_K_M | ~1.3 GB |

| Q4_K_M | ~1.1 GB |

| Q3_K_M | ~0.8 GB |

| Q2_K | ~0.6 GB |

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "Your prompt here" -n 512

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="model_q4_k_m.gguf",
    n_ctx=2048,
    n_gpu_layers=-1
)

response = llm(
    "Your prompt here",
    max_tokens=512,
    stop=["<|eot_id|>"]
)
print(response["choices"][0]["text"])

LM Studio

Download any .gguf file and load it directly in LM Studio.

Ollama

ollama run hf.co/tinyopsec/Vikhr-Llama-3.2-1B-Instruct-GGUF

Original Model

Vikhrmodels/Vikhr-Llama-3.2-1B-Instruct

Quantized using llama.cpp.