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rihuwa/Reina-Magistral-GGUF

sourceHugging Faceapache-2.0updated 10mo agoView on Hugging Face
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Model Card

Available Quantizations

QuantizationFile SizeUse CaseQuality
Q8_0~25 GBHighest quality99%+ of original
Q5KM~16 GBBest balance (RECOMMENDED)98%+ of original

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

bash
# 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 0.95

With Ollama

bash
# 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-magistral

Important Notes for Magistral Models

  1. 1.Special Tokens: Magistral uses [THINK] and [/THINK] tags for reasoning
  2. 2.Context Window: 128k tokens (optimal performance under 40k)
  3. 3.Chat Format: Mistral v7 Tekken template
  4. 4.Recommended Settings:
  5. 5.Temperature: 0.7
  6. 6.Top-p: 0.95
  7. 7.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

License

This model inherits the Apache 2.0 license from the base Magistral model.


Last Updated: 2025-11-23