🦾 Supported models and variants

Use one task notebook from examples/notebooks/, then choose a model key from this page.

Naming templates

Variant	Key template
Base	<model_key>
LoRA	<model_key>_lora
INT8	<model_key>_int8
LoRA + INT8	<model_key>_lora_int8
LoRA + K-bit (INT4 flow)	<model_key>_lora_kbit

Model keys

Model	Base key	Available variants
BLOOM 1.1B	bloom	base, lora, int8, lora_int8
Cerebras 1.3B	cerebras	base, lora, int8, lora_int8
DistilGPT-2	distilgpt2	base, lora
Falcon 7B	falcon	base, lora, int8, lora_int8, lora_kbit
Galactica 6.7B	galactica	base, lora, int8, lora_int8
Generic wrapper	generic	base, lora, int8, lora_int8, lora_kbit
GPT-J 6B	gptj	base, lora, int8, lora_int8
GPT-2	gpt2	base, lora, int8, lora_int8
GPT-OSS 20B	gpt_oss_20b	base, lora, int8, lora_int8, lora_kbit
GPT-OSS 120B	gpt_oss_120b	base, lora, int8, lora_int8, lora_kbit
LLaMA	llama	base, lora, int8, lora_int8, lora_kbit
LLaMA 2	llama2	base, lora, int8, lora_int8, lora_kbit
Mamba	mamba	base
MiniMaxM2	minimax_m2	base, lora, int8, lora_int8, lora_kbit
OPT 1.3B	opt	base, lora, int8, lora_int8
Qwen3 0.6B	qwen3_0_6b	base, lora, int8, lora_int8, lora_kbit
Stable Diffusion	stable_diffusion	base

INT4-style workflow

For models that expose *_lora_kbit, you can still use the generic K-bit API directly:

from xturing.models import GenericLoraKbitModel
model = GenericLoraKbitModel("/path/to/model")