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fix: add revision parameter to FastBaseModel in vision.py

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Propagate revision parameter to all from_pretrained calls in vision.py
to ensure consistent version pinning for vision models.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
(cherry picked from commit c5aa4ec927)
This commit is contained in:
majiayu000 2025-12-30 14:16:23 +08:00 • committed by Daniel Han
commit ae38c3639d
1 changed files with 4 additions and 1 deletions
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5
unsloth/models/vision.py
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@ -421,6 +421,7 @@ class FastBaseModel:
auto_config = None,
offload_embedding = False,
float32_mixed_precision = None, # Forces float32 mixed precision
revision = None,
# vLLM parameters
fast_inference = False,
gpu_memory_utilization = 0.5,
@ -720,6 +721,7 @@ class FastBaseModel:
model_name,
token = token,
trust_remote_code = trust_remote_code,
revision = revision,
)
if hasattr(auto_config, "quantization_config"):
from transformers.quantizers.auto import (
@ -776,12 +778,12 @@ class FastBaseModel:
model_name,
token = token,
trust_remote_code = trust_remote_code,
revision = revision,
)
setattr(auto_config, "_attn_implementation", config_attn_impl)
if hasattr(auto_config, "attn_implementation"):
setattr(auto_config, "attn_implementation", config_attn_impl)
model_config = auto_config
verify_fp8_support_if_applicable(model_config)
raise_handler = RaiseUninitialized()
@ -796,6 +798,7 @@ class FastBaseModel:
# quantization_config = bnb_config,
token = token,
trust_remote_code = trust_remote_code,
revision = revision,
# attn_implementation = attn_implementation,
**kwargs,
)