rihuwa/Reina-Magistral-GGUF
034
Available Quantizations
Training Details
- LoRA Rank: 128
- LoRA Alpha: 64
- Target Modules: All attention and FFN layers
- Training Format: Mistral v7 Tekken chat template
- Sequence Length: 2048 tokens
- Base Precision: BF16
Usage
With llama.cpp
# Download model (requires HF authentication for private repo)
huggingface-cli login
huggingface-cli download rihuwa/Reina_Magitral_gguf magistral-small-2509-text-only-Q5_K_M.gguf --local-dir ./models
# Run inference
./llama-cli -m ./models/magistral-small-2509-text-only-Q5_K_M.gguf -p "Your prompt here" -n 256 \
--temp 0.7 --top-p 0.95With Ollama
# Download the GGUF file first
huggingface-cli login
huggingface-cli download rihuwa/Reina_Magitral_gguf magistral-small-2509-text-only-Q5_K_M.gguf --local-dir ./models
# Create Modelfile (the template uses Mistral v7 format)
cat > Modelfile << 'EOF'
FROM ./models/magistral-small-2509-text-only-Q5_K_M.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER num_ctx 8192
EOF
# Create and run model
ollama create reina-magistral -f Modelfile
ollama run reina-magistralImportant Notes for Magistral Models
- Special Tokens: Magistral uses
[THINK]and[/THINK]tags for reasoning - Context Window: 128k tokens (optimal performance under 40k)
- Chat Format: Mistral v7 Tekken template
- Recommended Settings:
- Temperature: 0.7
- Top-p: 0.95
- Top-k: 40
Model Architecture
- Type: Mistral-based (24B parameters)
- Tokenizer: Tekken tokenizer with special reasoning tokens
- Vocabulary: Extended for reasoning capabilities
- Attention: Sliding window + global attention
Quantization Details
Both quantizations preserve the quality of the LoRA fine-tuning:
- Q8_0: 8-bit quantization, virtually no quality loss
- Q5_K_M: 5-bit with K-quant method, excellent quality/size balance
Performance
Tested on NVIDIA GPUs:
- Q8_0: Requires ~28GB VRAM
- Q5_K_M: Requires ~18GB VRAM (fits on single 24GB GPU)
Related Repositories
- LoRA Adapter: rihuwa/Reina_Magistral_LoRA
- Base Model: Darkhn/Magistral-Small-2509-Text-Only
License
This model inherits the Apache 2.0 license from the base Magistral model.
Last Updated: 2025-11-23
