---

license: apache-2.0

base_model: ai-nexuz/llama-3.2-1b-instruct-fine-tuned

tags:

- llama

- llama-3

- gguf

- quantized

- text-generation

- unsloth

- trl

language:

- en

pipeline_tag: text-generation

library_name: gguf

---

llama-3.2-1b-instruct-fine-tuned GGUF

GGUF quantizations of ai-nexuz/llama-3.2-1b-instruct-fine-tuned.

Fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the kanhatakeyama/wizardlm8x22b-logical-math-coding-sft dataset.

Specializes in logical reasoning, mathematics, and code generation.

---

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 / size tradeoff |

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

| model_q5_k_m.gguf | 5 | ~0.9 GB | Recommended |

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

| model_q4_k_m.gguf | 4 | ~0.75 GB | Good balance |

| model_q4_k_s.gguf | 4 | ~0.70 GB | Smaller Q4 |

| model_q3_k_l.gguf | 3 | ~0.60 GB | Low RAM, large variant |

| model_q3_k_m.gguf | 3 | ~0.57 GB | Low RAM |

| model_q3_k_s.gguf | 3 | ~0.53 GB | Minimum RAM Q3 |

| model_q2_k.gguf | 2 | ~0.45 GB | Extreme compression |

---

VRAM Requirements

| Quant | Min VRAM |

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

| F16 | 4 GB |

| Q8_0 | 2 GB |

| Q4_K_M | 1.5 GB |

| Q2_K | 1 GB |

---

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "Solve: 2x + 5 = 13" -n 256

llama-cpp-python

from llama_cpp import Llama
llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=2048)
output = llm("Solve: 2x + 5 = 13", max_tokens=256)
print(output["choices"][0]["text"])

LM Studio

Search tinyopsec/llama-3.2-1b-instruct-fine-tuned-GGUF in the model browser.

Ollama

ollama run hf.co/tinyopsec/llama-3.2-1b-instruct-fine-tuned-GGUF:Q4_K_M

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Original Model

ai-nexuz/llama-3.2-1b-instruct-fine-tuned