---

license: llama3

base_model: Iker/Llama-3-Instruct-Neurona-8b

tags:

- llama

- llama3

- gguf

- spanish

- english

- conversational

- synthetic

- function-calling

- text-generation-inference

language:

- es

- en

pipeline_tag: text-generation

library_name: gguf

---

Llama-3-Instruct-Neurona-8b GGUF

GGUF quantizations of Iker/Llama-3-Instruct-Neurona-8b.

Neurona 8B is a Spanish-first instruction-tuned language model based on Meta-Llama-3-8B-Instruct, fine-tuned on a mixture of 24 Spanish and English datasets covering RAG, function calling, code assistance, summarization, translation, and question answering. Trained with Axolotl on 4×A100 80GB GPUs.

---

Quantization Summary

| File | Bits | Size | Use Case |

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

| model_f16.gguf | 16 | ~16.1 GB | Full precision reference |

| model_q8_0.gguf | 8 | ~8.5 GB | Best quality, high VRAM |

| model_q6_k.gguf | 6 | ~6.6 GB | Near-lossless, recommended |

| model_q5_k_m.gguf | 5 | ~5.7 GB | Balanced quality/size |

| model_q5_k_s.gguf | 5 | ~5.4 GB | Slightly smaller Q5 |

| model_q4_k_m.gguf | 4 | ~4.9 GB | Good quality, everyday use |

| model_q4_k_s.gguf | 4 | ~4.6 GB | Smaller Q4 |

| model_q3_k_l.gguf | 3 | ~4.0 GB | Low VRAM, acceptable quality |

| model_q3_k_m.gguf | 3 | ~3.7 GB | Lower VRAM |

| model_q3_k_s.gguf | 3 | ~3.4 GB | Minimum quality Q3 |

| model_q2_k.gguf | 2 | ~2.9 GB | Very low VRAM, degraded quality |

---

VRAM Requirements

| Quantization | VRAM (model only) | Fits on |

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

| Q8_0 | ~9 GB | RTX 3090/4090, A10 |

| Q6_K | ~7 GB | RTX 3080, A10 |

| Q5_K_M | ~6 GB | RTX 3070/4070 |

| Q4_K_M | ~5 GB | RTX 3060/4060 |

| Q3_K_M | ~4 GB | RTX 3050, GTX 1660 |

| Q2_K | ~3 GB | Low-end GPUs, CPU |

---

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "Hola, ¿cómo estás?" -n 256

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=8192)
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "¿Cuál es la capital de España?"}]
)
print(response["choices"][0]["message"]["content"])

LM Studio

1. Open LM Studio → Search tinyopsec/Llama-3-Instruct-Neurona-8b-GGUF
2. Download preferred quantization
3. Load and chat

Ollama

ollama run hf.co/tinyopsec/Llama-3-Instruct-Neurona-8b-GGUF:Q4_K_M

---

Chat Template

Uses LLaMA 3 chat template with <|begin_of_text|>, <|start_header_id|>, <|end_header_id|>, <|eot_id|> tokens.

---

Original Model

Iker/Llama-3-Instruct-Neurona-8b — base: meta-llama/Meta-Llama-3-8B-Instruct, license: llama3.