Phi-4 DeepSeek Distilled v8 GGUF

This repository contains GGUF quantized versions of the Phi-4 DeepSeek R1 Distilled model. These GGUF files are optimized for local inference using frameworks like llama.cpp and Ollama and LM Studio.

Model Information

• Base Model: Phi-4 DeepSeek R1 Distilled
• Parameters: 14.7B
• Architecture: Phi3
• Context Length: 16384 tokens
• Training Data: Improved version of Phi-4, distilled with DeepSeek R1 Reasoning
• License: MIT

Available Quantizations

Chat Template

This model uses the ChatML format with the following structure:

<|im_start|>system<|im_sep|>System message here<|im_end|>
<|im_start|>user<|im_sep|>User message here<|im_end|>
<|im_start|>assistant<|im_sep|>Assistant response here<|im_end|>

Usage with Ollama

Create a custom Modelfile (paste this into a file named Modelfile):

---

FROM /replace/with/path/to/your/gguf-file.gguf

PARAMETER temperature 0.15 PARAMETER top_p 0.93 PARAMETER top_k 50 PARAMETER repeat_penalty 1.15

TEMPLATE """{{ if .System }}<|im_start|>system<|im_sep|>{{ .System }}<|im_end|>{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user<|im_sep|>{{ .Content }}<|im_en>"""

PARAMETER stop "<|im_start|>"

PARAMETER stop "<|im_end|>"

---

Then create and use your model:

ollama create phi4-deepseek-r1 -f Modelfile

ollama run phi4-deepseek-r1

Usage with LMStudio

1. Use the model search option to look up the model from huggingface
2. Download and Load the Model
3. Set the chat parameters (top_p, top_k, repeat_penalty etc...)
4. Chat with the model (LMStudio directly detects the chat template so there is no manual configuration here unlike Ollama)

Usage with llama.cpp

# Download the model from Hugging Face
wget https://huggingface.co/ykarout/phi4-deepseek-r1-distilled-v8-GGUF/resolve/main/phi4-deepseek-r1-distilled-v8-q8_0.gguf

# Run the model with llama.cpp
./main -m phi4-deepseek-r1-distilled-v8-q8_0.gguf -n 1024 --color -i -ins --chatml

Benchmarks & Performance Notes

• Q8_0: Best quality, requires ~16GB VRAM for 4K context
• Q3_K_M: Good quality with 60% size reduction, suitable for systems with 8GB+ VRAM