How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Melvin56/DeepSeek-R1-Distill-Llama-8B-Enkrypt-Aligned-GGUF",
	filename="",
)
llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Melvin56/DeepSeek-R1-Distill-Llama-8B-Enkrypt-Aligned-GGUF

Original Model : enkryptai/DeepSeek-R1-Distill-Llama-8B-Enkrypt-Aligned

All quants are made using the imatrix option.

Model Size (GB)
Q2_K 3.17
Q3_K_M 4.02
Q4_K_M 4.92
Q5_K_M 5.72
Q6_K 6.59
Q8_0 8.54
F16 16.2
CPU (AVX2) CPU (ARM NEON) Metal cuBLAS rocBLAS SYCL CLBlast Vulkan Kompute
K-quants ✅ 🐢5 ✅ 🐢5
I-quants ✅ 🐢4 ✅ 🐢4 ✅ 🐢4 Partial¹
✅: feature works
🚫: feature does not work
❓: unknown, please contribute if you can test it youself
🐢: feature is slow
¹: IQ3_S and IQ1_S, see #5886
²: Only with -ngl 0
³: Inference is 50% slower
⁴: Slower than K-quants of comparable size
⁵: Slower than cuBLAS/rocBLAS on similar cards
⁶: Only q8_0 and iq4_nl
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GGUF
Model size
8B params
Architecture
llama
Hardware compatibility
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