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model: add rnj-1 inference support (#13354)

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This commit is contained in:
Jeffrey Morgan
2025-12-08 16:49:17 -08:00
committed by GitHub
parent 603ceefaa6
commit d2f334c1f7
6 changed files with 208 additions and 69 deletions
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64
convert/convert_gemma3.go
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@@ -2,6 +2,7 @@ package convert
import (
"cmp"
"slices"
"github.com/ollama/ollama/fs/ggml"
)
@@ -26,16 +27,26 @@ type gemma3Model struct {
NumChannels uint32 `json:"num_channels"` // num_channels 3
PatchSize uint32 `json:"patch_size"` // patch_size 14
} `json:"vision_config"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
RMSNormEPS float32 `json:"rms_norm_eps"`
HeadDim uint32 `json:"head_dim"`
FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
RopeLocalTheta float32 `json:"rope_local_base_freq"`
RopeGlobalTheta float32 `json:"rope_global_base_freq"`
SlidingWindow uint32 `json:"sliding_window"`
MultiModalTokensPerImage uint32 `json:"mm_tokens_per_image"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
RMSNormEPS float32 `json:"rms_norm_eps"`
HeadDim uint32 `json:"head_dim"`
FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
RopeLocalTheta float32 `json:"rope_local_base_freq"`
RopeTheta float32 `json:"rope_theta"`
SlidingWindow uint32 `json:"sliding_window"`
SlidingWindowPattern *uint32 `json:"sliding_window_pattern"`
LayerTypes []string `json:"layer_types"`
MultiModalTokensPerImage uint32 `json:"mm_tokens_per_image"`
RopeScaling *struct {
Type string `json:"rope_type"`
Factor float32 `json:"factor"`
OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
ExtrapolationFactor float32 `json:"extrapolation_factor"`
BetaFast float32 `json:"beta_fast"`
BetaSlow float32 `json:"beta_slow"`
} `json:"rope_scaling"`
}
const (
@@ -81,9 +92,38 @@ func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
kv["gemma3.attention.key_length"] = p.HeadDim
kv["gemma3.attention.value_length"] = p.HeadDim
kv["gemma3.attention.sliding_window"] = p.SlidingWindow
kv["gemma3.final_logit_softcapping"] = cmp.Or(p.FinalLogitSoftcap, 30)
// The sliding window pattern is either provided as the sliding_window_pattern
// key (an int) or as the layer_types key (a list of strings).
if p.SlidingWindowPattern != nil || len(p.LayerTypes) > 0 {
kv["gemma3.attention.sliding_window_pattern"] = slices.Collect(func(yield func(bool) bool) {
for i := range numBlocks {
var isLocal bool
if len(p.LayerTypes) > 0 && int(i) < len(p.LayerTypes) {
isLocal = p.LayerTypes[i] == "sliding_attention"
} else if p.SlidingWindowPattern != nil && *p.SlidingWindowPattern > 0 {
isLocal = (i+1)%*p.SlidingWindowPattern != 0
}
if !yield(isLocal) {
break
}
}
})
}
if p.FinalLogitSoftcap > 0 {
kv["gemma3.final_logit_softcapping"] = p.FinalLogitSoftcap
}
kv["gemma3.rope.local.freq_base"] = cmp.Or(p.RopeLocalTheta, 10000.0)
kv["gemma3.rope.global.freq_base"] = cmp.Or(p.RopeGlobalTheta, 1000000.0)
kv["gemma3.rope.freq_base"] = cmp.Or(p.RopeTheta, 1000000.0)
if p.RopeScaling != nil && p.RopeScaling.Type == "yarn" && p.RopeScaling.Factor > 0 {
kv["gemma3.rope.scaling.type"] = "yarn"
kv["gemma3.rope.scaling.factor"] = p.RopeScaling.Factor
kv["gemma3.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeddings
kv["gemma3.rope.scaling.extrapolation_factor"] = cmp.Or(p.RopeScaling.ExtrapolationFactor, float32(1.0))
kv["gemma3.rope.scaling.beta_fast"] = cmp.Or(p.RopeScaling.BetaFast, float32(64.0))
kv["gemma3.rope.scaling.beta_slow"] = cmp.Or(p.RopeScaling.BetaSlow, float32(1.0))
}
kv["gemma3.embedding_length"] = p.HiddenSize
kv["gemma3.feed_forward_length"] = p.IntermediateSize
default: