diff --git a/src/huggingface/implementation/bart/bart.jl b/src/huggingface/implementation/bart/bart.jl
index 10e467a..a40f7b3 100644
--- a/src/huggingface/implementation/bart/bart.jl
+++ b/src/huggingface/implementation/bart/bart.jl
@@ -1 +1,3 @@
 include("./config.jl")
+include("./load.jl")
+
diff --git a/src/huggingface/implementation/bart/load.jl b/src/huggingface/implementation/bart/load.jl
new file mode 100644
index 0000000..5e2cfff
--- /dev/null
+++ b/src/huggingface/implementation/bart/load.jl
@@ -0,0 +1,239 @@
+using ..Layers
+using ..Layers: CompositeEmbedding, SelfAttention, CrossAttention, MultiheadQKVAttenOp, CausalMultiheadQKVAttenOp
+using Functors
+using NeuralAttentionlib
+using NeuralAttentionlib: WithScore
+
+bart_pe_shift(x) = bart_pe_shift(size(x, 2))
+bart_pe_shift(len::Integer) = bart_pe_shift(Base.OneTo(len))
+bart_pe_shift(x::AbstractArray{<:Integer}) = x .+ 2
+
+@hgfdef Bart (
+    Model => (embed, seq2seq),
+    # ForConditionalGeneration,
+    # ForSequenceClassification,
+    # ForQuestionAnswering,
+    # ForCausalLM,
+)
+
+basemodelkey(::Type{<:HGFBartPreTrainedModel}) = :model
+
+function load_model(_type::Type{HGFBartModel}, cfg, state_dict, prefix)
+    embed = load_model(_type, CompositeEmbedding, cfg, state_dict, prefix)
+    encoder = load_model(_type, TransformerBlock, cfg, state_dict, joinname(prefix, "encoder"))
+    decoder = load_model(_type, TransformerDecoderBlock, cfg, state_dict, joinname(prefix, "decoder"))
+    seq2seq = Seq2Seq(encoder, decoder)
+    return HGFBartModel(embed, seq2seq)
+end
+
+function load_model(::Type{<:HGFBartPreTrainedModel}, ::Type{Layers.Embed}, cfg, state_dict, prefix)
+    vocab_size, dims, pad_id = cfg[:vocab_size], cfg[:d_model], cfg[:pad_token_id]
+    factor = Float32(cfg[:init_std])
+    scale = cfg.scale_embedding ? Float32(sqrt(dims)) : nothing
+    token_weight = getweight(Layers.Embed, state_dict, joinname(prefix, "weight")) do
+        weight = weight_init(vocab_size, dims, factor)()
+        weight[:, pad_id+1] .= 0
+        return weight
+    end
+    return Layers.Embed(scale, token_weight)
+end
+function load_model(::Type{<:HGFBartPreTrainedModel}, ::Type{Layers.FixedLenPositionEmbed}, cfg, state_dict, prefix)
+    dims = cfg[:d_model]
+    max_pos = cfg[:max_position_embeddings]
+    factor = Float32(cfg[:init_std])
+    pos_weight = getweight(weight_init(max_pos + 2 #= following HGF Bart's hack =#, dims, factor),
+                           Layers.Embed, state_dict, joinname(prefix, "embed_positions.weight"))
+    return Layers.FixedLenPositionEmbed(pos_weight)
+end
+
+function load_model(_type::Type{<:HGFBartPreTrainedModel}, ::Type{CompositeEmbedding}, cfg, state_dict, prefix)
+    dims = cfg[:d_model]
+    token_embed = load_model(_type, Layers.Embed, cfg, state_dict, joinname(prefix, "shared"))
+    enc_pos = load_model(_type, Layers.FixedLenPositionEmbed, cfg, state_dict, joinname(prefix, "encoder"))
+    dec_pos = load_model(_type, Layers.FixedLenPositionEmbed, cfg, state_dict, joinname(prefix, "decoder"))
+    enc_embed = CompositeEmbedding(token = token_embed, position = (.+, enc_pos, bart_pe_shift))
+    dec_embed = CompositeEmbedding(token = token_embed, position = (.+, dec_pos, bart_pe_shift))
+    enc_ln = _load_layernorm(state_dict, joinname(prefix, "encoder.layernorm_embedding"), dims)
+    dec_ln = _load_layernorm(state_dict, joinname(prefix, "decoder.layernorm_embedding"), dims)
+    return Layers.Parallel{(:encoder_input, :decoder_input)}((
+        Layers.Chain(enc_embed, enc_ln),
+        Layers.Chain(dec_embed, dec_ln)))
+end
+
+load_model(::Type{<:HGFBartPreTrainedModel}, ::Type{Layers.LayerNorm}, cfg, state_dict, prefix) =
+    _load_layernorm(state_dict, prefix, cfg[:d_model])
+
+function load_model(
+    ::Type{<:HGFBartPreTrainedModel},
+    ::Type{<:SelfAttention{A}},
+    cfg, state_dict, prefix
+) where {A <: Union{MultiheadQKVAttenOp, CausalMultiheadQKVAttenOp}}
+    dims = cfg[:d_model]
+    head = cfg[A <: CausalMultiheadQKVAttenOp ? :encoder_attention_heads : :decoder_attention_heads]
+    p = Float64(cfg[:attention_dropout]); p = iszero(p) ? nothing : p
+    return_score = cfg[:output_attentions]
+    factor = Float32(cfg[:init_std])
+    qkv_proj = Layers.Fork(
+        _load_dense(state_dict, joinname(prefix, "q_proj"), dims, dims, factor, true),
+        _load_dense(state_dict, joinname(prefix, "k_proj"), dims, dims, factor, true),
+        _load_dense(state_dict, joinname(prefix, "v_proj"), dims, dims, factor, true),
+    )
+    o_proj = _load_dense(state_dict, joinname(prefix, "out_proj"), dims, dims, factor, true)
+    if A <: CausalMultiheadQKVAttenOp
+        op = CausalMultiheadQKVAttenOp(head, p)
+    else
+        op = MultiheadQKVAttenOp(head, p)
+    end
+    return_score && (op = WithScore(op))
+    return SelfAttention(op, qkv_proj, o_proj)
+end
+function load_model(::Type{<:HGFBartPreTrainedModel}, ::Type{Layers.CrossAttention}, cfg, state_dict, prefix)
+    head, dims = cfg[:decoder_attention_heads], cfg[:d_model]
+    p = Float64(cfg[:attention_dropout]); p = iszero(p) ? nothing : p
+    return_score = cfg[:output_attentions]
+    factor = Float32(cfg[:init_std])
+    q_proj = _load_dense(state_dict, joinname(prefix, "q_proj"), dims, dims, factor, true)
+    kv_proj = Layers.Fork(
+        _load_dense(state_dict, joinname(prefix, "k_proj"), dims, dims, factor, true),
+        _load_dense(state_dict, joinname(prefix, "v_proj"), dims, dims, factor, true),
+    )
+    o_proj = _load_dense(state_dict, joinname(prefix, "out_proj"), dims, dims, factor, true)
+    op = MultiheadQKVAttenOp(head, p)
+    return_score && (op = WithScore(op))
+    return CrossAttention(op, q_proj, kv_proj, o_proj)
+end
+
+function _load_bart_ffn(state_dict, prefix, dims, ff_dims, factor, p, act)
+    fc1 = _load_dense(state_dict, joinname(prefix, "fc1"), dims, ff_dims, factor, true, act)
+    fc2 = _load_dense(state_dict, joinname(prefix, "fc2"), ff_dims, dims, factor, true)
+    return Layers.Chain(Layers.DropoutLayer(fc1, p), fc2)
+end
+
+function load_model(_type::Type{<:HGFBartPreTrainedModel}, ::Type{<:TransformerBlock}, cfg, state_dict, prefix)
+    dims = cfg[:d_model]
+    ff_dims = cfg[:encoder_ffn_dim]
+    factor = Float32(cfg[:init_std])
+    act_p = Float64(cfg[:activation_dropout]); act_p = iszero(act_p) ? nothing : act_p
+    act = ACT2FN[Symbol(cfg[:activation_function])]
+    n = cfg[:encoder_layers]
+    p = Float64(cfg[:dropout]); p = iszero(p) ? nothing : p
+    collect_output = cfg[:output_attentions] || cfg[:output_hidden_states]
+    blocks = []
+    for i = 1:n
+        lprefix = joinname(prefix, :layers, i-1)
+        sa = load_model(_type, SelfAttention{MultiheadQKVAttenOp}, cfg, state_dict, joinname(lprefix, "self_attn"))
+        sa_ln = load_model(_type, Layers.LayerNorm, cfg, state_dict, joinname(lprefix, "self_attn_layer_norm"))
+        sa = Layers.PostNormResidual(Layers.DropoutLayer(sa, p), sa_ln)
+        ff = _load_bart_ffn(state_dict, lprefix, dims, ff_dims, factor, act_p, act)
+        ff_ln = load_model(_type, Layers.LayerNorm, cfg, state_dict, joinname(lprefix, "final_layer_norm"))
+        ff = Layers.PostNormResidual(Layers.DropoutLayer(ff, p), ff_ln)
+        block = TransformerBlock(sa, ff)
+        push!(blocks, block)
+    end
+    collect_f = collect_output ? Layers.collect_outputs : nothing
+    trf = Transformer(Tuple(blocks), collect_f)
+    return trf
+end
+
+function load_model(_type::Type{<:HGFBartPreTrainedModel}, ::Type{<:TransformerDecoderBlock}, cfg, state_dict, prefix)
+    dims = cfg[:d_model]
+    ff_dims = cfg[:decoder_ffn_dim]
+    factor = Float32(cfg[:init_std])
+    act_p = Float64(cfg[:activation_dropout]); act_p = iszero(act_p) ? nothing : act_p
+    act = ACT2FN[Symbol(cfg[:activation_function])]
+    n = cfg[:decoder_layers]
+    p = Float64(cfg[:dropout]); p = iszero(p) ? nothing : p
+    collect_output = cfg[:output_attentions] || cfg[:output_hidden_states]
+    blocks = []
+    for i = 1:n
+        lprefix = joinname(prefix, :layers, i-1)
+        sa = load_model(_type, SelfAttention{CausalMultiheadQKVAttenOp}, cfg, state_dict, joinname(lprefix, "self_attn"))
+        sa_ln = load_model(_type, Layers.LayerNorm, cfg, state_dict, joinname(lprefix, "self_attn_layer_norm"))
+        sa = Layers.PostNormResidual(Layers.DropoutLayer(sa, p), sa_ln)
+        ca = load_model(_type, CrossAttention, cfg, state_dict, joinname(lprefix, "encoder_attn"))
+        ca_ln = load_model(_type, Layers.LayerNorm, cfg, state_dict, joinname(lprefix, "encoder_attn_layer_norm"))
+        ca = Layers.PostNormResidual(Layers.DropoutLayer(ca, p), ca_ln)
+        ff = _load_bart_ffn(state_dict, lprefix, dims, ff_dims, factor, act_p, act)
+        ff_ln = load_model(_type, Layers.LayerNorm, cfg, state_dict, joinname(lprefix, "final_layer_norm"))
+        ff = Layers.PostNormResidual(Layers.DropoutLayer(ff, p), ff_ln)
+        block = TransformerDecoderBlock(sa, ca, ff)
+        push!(blocks, block)
+    end
+    collect_f = collect_output ? Layers.collect_outputs : nothing
+    trf = Transformer(Tuple(blocks), collect_f)
+    return trf
+end
+
+function get_state_dict(m::HGFBartModel, state_dict, prefix)
+    get_state_dict(HGFBartModel, m.embed, state_dict, prefix)
+    get_state_dict(HGFBartModel, m.seq2seq, state_dict, prefix)
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::Layers.Parallel, state_dict, prefix)
+    # m.layer.encoder_input[1].token === m.layer.decoder_input[1].token for BartModel
+    get_state_dict(p, m.layer.encoder_input[1].token, state_dict, joinname(prefix, "shared"))
+    get_state_dict(p, m.layer.encoder_input, state_dict, joinname(prefix, "encoder"))
+    get_state_dict(p, m.layer.decoder_input, state_dict, joinname(prefix, "decoder"))
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::Layers.Chain{<:Tuple{CompositeEmbedding, Any}}, state_dict, prefix)
+    get_state_dict(p, m[1].token, state_dict, joinname(prefix, "embed_tokens"))
+    get_state_dict(p, m[1].position.embed, state_dict, joinname(prefix, "embed_positions"))
+    get_state_dict(p, m[2], state_dict, joinname(prefix, "layernorm_embedding"))
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::Seq2Seq, state_dict, prefix)
+    get_state_dict(p, m.encoder, state_dict, joinname(prefix, "encoder"))
+    get_state_dict(p, m.decoder, state_dict, joinname(prefix, "decoder"))
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::Transformer, state_dict, prefix)
+    for (i, t) in enumerate(m.blocks)
+        get_state_dict(p, t, state_dict, joinname(prefix, "layers", i-1))
+    end
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::Layers.Chain{<:Tuple{Any, Layers.Dense}},
+                        state_dict, prefix)
+    get_state_dict(p, m[1], state_dict, joinname(prefix, "fc1"))
+    get_state_dict(p, m[2], state_dict, joinname(prefix, "fc2"))
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::SelfAttention, state_dict, prefix)
+    get_state_dict(p, m.qkv_proj.layers[1], state_dict, joinname(prefix, "q_proj"))
+    get_state_dict(p, m.qkv_proj.layers[2], state_dict, joinname(prefix, "k_proj"))
+    get_state_dict(p, m.qkv_proj.layers[3], state_dict, joinname(prefix, "v_proj"))
+    get_state_dict(p, m.o_proj, state_dict, joinname(prefix, "out_proj"))
+    return state_dict
+end
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::CrossAttention, state_dict, prefix)
+    get_state_dict(p, m.q_proj, state_dict, joinname(prefix, "q_proj"))
+    get_state_dict(p, m.kv_proj.layers[1], state_dict, joinname(prefix, "k_proj"))
+    get_state_dict(p, m.kv_proj.layers[2], state_dict, joinname(prefix, "v_proj"))
+    get_state_dict(p, m.o_proj, state_dict, joinname(prefix, "out_proj"))
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::TransformerBlock, state_dict, prefix)
+    get_state_dict(p, m.attention.layer, state_dict, joinname(prefix, "self_attn"))
+    get_state_dict(p, m.attention.norm, state_dict, joinname(prefix, "self_attn_layer_norm"))
+    get_state_dict(p, m.feedforward.layer, state_dict, prefix)
+    get_state_dict(p, m.feedforward.norm, state_dict, joinname(prefix, "final_layer_norm"))
+    return state_dict
+end
+
+function get_state_dict(p::Type{<:HGFBartPreTrainedModel}, m::TransformerDecoderBlock, state_dict, prefix)
+    get_state_dict(p, m.attention.layer, state_dict, joinname(prefix, "self_attn"))
+    get_state_dict(p, m.attention.norm, state_dict, joinname(prefix, "self_attn_layer_norm"))
+    get_state_dict(p, m.crossattention.layer, state_dict, joinname(prefix, "encoder_attn"))
+    get_state_dict(p, m.crossattention.norm, state_dict, joinname(prefix, "encoder_attn_layer_norm"))
+    get_state_dict(p, m.feedforward.layer, state_dict, prefix)
+    get_state_dict(p, m.feedforward.norm, state_dict, joinname(prefix, "final_layer_norm"))
+    return state_dict
+end
diff --git a/src/huggingface/models/load.jl b/src/huggingface/models/load.jl
index 32a3734..5f5102c 100644
--- a/src/huggingface/models/load.jl
+++ b/src/huggingface/models/load.jl
@@ -243,7 +243,7 @@ function _load_embed(state_dict, prefix, w_init, pad_idx0 = nothing)
     return Layers.Embed(embedding)
 end
 
-function _load_layernorm(state_dict, prefix, dims, ln_ϵ)
+function _load_layernorm(state_dict, prefix, dims, ln_ϵ = 1e-5)
     old_weight_name = joinname(prefix, "gamma")
     old_bias_name = joinname(prefix, "beta")
     weight_name = haskey(state_dict, old_weight_name) ? old_weight_name : joinname(prefix, "weight")
diff --git a/test/huggingface/load.jl b/test/huggingface/load.jl
index 4008afa..afa4f99 100644
--- a/test/huggingface/load.jl
+++ b/test/huggingface/load.jl
@@ -2,6 +2,7 @@
     using Logging
     using Transformers.HuggingFace
     model_list = Dict([
+        :bart => [:Model],
         :bert => :[
             Model, ForPreTraining, LMHeadModel, ForMaskedLM, ForNextSentencePrediction,
             ForSequenceClassification, ForTokenClassification, ForQuestionAnswering,