---

license: apache-2.0

base_model: l3lab/L1-Qwen3-8B-Exact

tags:

- gguf

- quantization

- qwen3

language: en

pipeline_tag: text-generation

library_name: gguf

---

L1-Qwen3-8B-Exact GGUF

GGUF quantizations of l3lab/L1-Qwen3-8B-Exact.

Quantization Variants

| File | Bits | Size | Use Case |

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

| model_f16.gguf | 16 | ~16 GB | Reference, maximum quality |

| model_q8_0.gguf | 8 | ~8.5 GB | High quality, still large |

| model_q6_k.gguf | 6 | ~6.5 GB | Balanced quality/size |

| model_q5_k_m.gguf | 5 | ~5.3 GB | Good quality, recommended |

| model_q5_k_s.gguf | 5 | ~5.1 GB | Slightly faster Q5 |

| model_q4_k_m.gguf | 4 | ~4.3 GB | Standard quantization |

| model_q4_k_s.gguf | 4 | ~4.1 GB | Slightly faster Q4 |

| model_q3_k_l.gguf | 3 | ~3.3 GB | Small model variant |

| model_q3_k_m.gguf | 3 | ~3.2 GB | Balanced 3-bit |

| model_q3_k_s.gguf | 3 | ~3.0 GB | Fast 3-bit |

| model_q2_k.gguf | 2 | ~2.3 GB | Minimal size, CPU-friendly |

VRAM Requirements

| Quantization | GPU VRAM | RAM (CPU) |

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

| F16 | ~16 GB | ~20 GB |

| Q8_0 | ~9 GB | ~11 GB |

| Q6_K | ~7 GB | ~9 GB |

| Q5_K_M | ~6 GB | ~8 GB |

| Q4_K_M | ~5 GB | ~6 GB |

| Q3_K_M | ~3.5 GB | ~5 GB |

| Q2_K | ~2.5 GB | ~3.5 GB |

Usage

llama.cpp

./main -m model_q5_k_m.gguf -n 256 -p "Your prompt here"

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="model_q5_k_m.gguf",
    n_gpu_layers=-1,  # Use GPU
    n_ctx=2048,
)

response = llm("Your prompt here", max_tokens=256)
print(response["choices"][0]["text"])

LM Studio

1. Download any .gguf file from this repo
2. Open in LM Studio
3. Configure context and parameters
4. Start chat

Ollama

ollama create l1-qwen3-8b -f Modelfile

Modelfile:

FROM model_q5_k_m.gguf
PARAMETER temperature 0.7

Base Model

l3lab/L1-Qwen3-8B-Exact — Fine-tuned reasoning model based on Qwen3 8B, trained with reinforcement learning to optimize reasoning step count.

License

Apache 2.0 (as per base model)