Import

GGUF models and select Safetensors models can be imported directly into Ollama.

Import GGUF

A binary GGUF file can be imported directly into Ollama through a Modelfile.

FROM /path/to/file.gguf

Import Safetensors

If the model being imported is one of these architectures, it can be imported directly into Ollama through a Modelfile:

LlamaForCausalLM
MistralForCausalLM
MixtralForCausalLM
GemmaForCausalLM
Phi3ForCausalLM

FROM /path/to/safetensors/directory

For architectures not directly convertable by Ollama, see llama.cpp's guide on conversion. After conversion, see Import GGUF.

Automatic Quantization

Note

Automatic quantization requires v0.1.35 or higher.

Ollama is capable of quantizing FP16 or FP32 models to any of the supported quantizations with the -q/--quantize flag in ollama create.

FROM /path/to/my/gemma/f16/model

$ ollama create -q Q4_K_M mymodel
transferring model data
quantizing F16 model to Q4_K_M
creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
creating new layer sha256:0853f0ad24e5865173bbf9ffcc7b0f5d56b66fd690ab1009867e45e7d2c4db0f
writing manifest
success

Supported Quantizations

Q4_0
Q4_1
Q5_0
Q5_1
Q8_0

K-means Quantizations

Q3_K_S
Q3_K_M
Q3_K_L
Q4_K_S
Q4_K_M
Q5_K_S
Q5_K_M
Q6_K

Template Detection

Note

Template detection requires v0.1.42 or higher.

Ollama uses model metadata, specifically tokenizer.chat_template, to automatically create a template appropriate for the model you're importing.

FROM /path/to/my/gemma/model

$ ollama create mymodel
transferring model data
using autodetected template gemma-instruct
creating new layer sha256:baa2a0edc27d19cc6b7537578a9a7ba1a4e3214dc185ed5ae43692b319af7b84
creating new layer sha256:ba66c3309914dbef07e5149a648fd1877f030d337a4f240d444ea335008943cb
writing manifest
success

Defining a template in the Modelfile will disable this feature which may be useful if you want to use a different template than the autodetected one.

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