初始化项目，由ModelHub XC社区提供模型
Model: chestnutlzj/MoE-Qwen-4x1.8B-pretrain-50000-ckpt Source: Original Platform
This commit is contained in:
ModelHub XC
52
.gitattributes
vendored
Normal file
52
.gitattributes
vendored
Normal file
@@ -0,0 +1,52 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
||||
*.tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
*.db* filter=lfs diff=lfs merge=lfs -text
|
||||
*.ark* filter=lfs diff=lfs merge=lfs -text
|
||||
**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
|
||||
**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
|
||||
**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
|
||||
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.gguf* filter=lfs diff=lfs merge=lfs -text
|
||||
*.ggml filter=lfs diff=lfs merge=lfs -text
|
||||
*.llamafile* filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
|
||||
model-00002-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
loss_plot.png filter=lfs diff=lfs merge=lfs -text
|
||||
qwen.tiktoken filter=lfs diff=lfs merge=lfs -text
|
||||
model-00001-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
40
README.md
Normal file
40
README.md
Normal file
@@ -0,0 +1,40 @@
|
||||
---
|
||||
license: apache-2.0
|
||||
language:
|
||||
- zh
|
||||
---
|
||||
# 4x1.8B MoE Qwen Ckpt 50000
|
||||
|
||||
This is a MoE model project constructed based on the Qwen 1.8B model. In this project, we concatenated 4 original models and trained them using special training methods.
|
||||
|
||||
This model is a checkpoint model for the continue pretraining stage.
|
||||
|
||||
|
||||
|
||||
# Evaluations
|
||||
|
||||
| Groups |n-shot| Metric |Value | |Stderr|
|
||||
|------------------|-----:|--------|-----:|---|-----:|
|
||||
|boolq | 0|acc |0.6508|± |0.0083|
|
||||
|ceval-valid | 0|acc |0.5290|± |0.1912|
|
||||
| | 0|acc_norm|0.5290|± |0.1912|
|
||||
|cmmlu | 0|acc |0.5087|± |0.1237|
|
||||
| | 0|acc_norm|0.5087|± |0.1237|
|
||||
|mathqa | 0|acc |0.2647|± |0.0081|
|
||||
| | 0|acc_norm|0.2693|± |0.0081|
|
||||
|mmlu | 0|acc |0.4353|± |0.0830|
|
||||
| - stem | 0|acc |0.3809|± |0.0659|
|
||||
| - social_sciences| 0|acc |0.4959|± |0.0708|
|
||||
| - other | 0|acc |0.4844|± |0.0744|
|
||||
| - humanities | 0|acc |0.3998|± |0.0849|
|
||||
|
||||
# Acknowledgements
|
||||
|
||||
+ [Qwen](https://github.com/QwenLM/Qwen)
|
||||
+ [mistral.ai](https://mistral.ai)
|
||||
|
||||
# License Agreement
|
||||
|
||||
This project is open source under the Tongyi Qianwen Research License Agreement. You can view the complete license agreement in this link: [https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20RESEARCH%20LICENSE%20AGREEMENT].
|
||||
|
||||
During the use of this project, please ensure that your usage behavior complies with the terms and conditions of the license agreement.
|
||||
31
config.json
Normal file
31
config.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"_name_or_path": "/data03/moe_chatlaw/models/moe_chatlaw_pt_4/checkpoint-26000",
|
||||
"architectures": [
|
||||
"MixtralForCausalLM"
|
||||
],
|
||||
"attention_bias": true,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 5504,
|
||||
"max_position_embeddings": 8192,
|
||||
"model_type": "mixtral",
|
||||
"num_attention_heads": 16,
|
||||
"num_experts_per_tok": 2,
|
||||
"num_hidden_layers": 24,
|
||||
"num_key_value_heads": 16,
|
||||
"num_local_experts": 4,
|
||||
"output_router_logits": true,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_theta": 10000.0,
|
||||
"router_aux_loss_coef": 0.001,
|
||||
"sliding_window": 4096,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.36.2",
|
||||
"use_cache": true,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"transformers_version": "4.36.2"
|
||||
}
|
||||
3
loss_plot.png
Normal file
3
loss_plot.png
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:93ef77aafc4823302da7032ee042368f6506ae348a582769b81a20720f7f33a4
|
||||
size 1055988
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f79d23cd83ba12f9a072ca6be145b05ccf91a90421811b2f57db6dd953d468e8
|
||||
size 4981245312
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:facfec55cca83a8e0bd2e817ab54b336151f1cbe5630cd76cc19489f197552ed
|
||||
size 3562355120
|
||||
466
model.safetensors.index.json
Normal file
466
model.safetensors.index.json
Normal file
@@ -0,0 +1,466 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 8543543296
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00002-of-00002.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.0.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.1.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.2.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.3.w1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.block_sparse_moe.gate.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.3.w2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.block_sparse_moe.gate.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
3
qwen.tiktoken
Normal file
3
qwen.tiktoken
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b2b1b8dfb5cc5f024bafc373121c6aba3f66f9a5a0269e243470a1de16a33186
|
||||
size 2561218
|
||||
3
special_tokens_map.json
Normal file
3
special_tokens_map.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"pad_token": "<|endoftext|>"
|
||||
}
|
||||
276
tokenization_qwen.py
Normal file
276
tokenization_qwen.py
Normal file
@@ -0,0 +1,276 @@
|
||||
# Copyright (c) Alibaba Cloud.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
"""Tokenization classes for QWen."""
|
||||
|
||||
import base64
|
||||
import logging
|
||||
import os
|
||||
import unicodedata
|
||||
from typing import Collection, Dict, List, Set, Tuple, Union
|
||||
|
||||
import tiktoken
|
||||
from transformers import PreTrainedTokenizer, AddedToken
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
|
||||
|
||||
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
||||
ENDOFTEXT = "<|endoftext|>"
|
||||
IMSTART = "<|im_start|>"
|
||||
IMEND = "<|im_end|>"
|
||||
# as the default behavior is changed to allow special tokens in
|
||||
# regular texts, the surface forms of special tokens need to be
|
||||
# as different as possible to minimize the impact
|
||||
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
||||
# changed to use actual index to avoid misconfiguration with vocabulary expansion
|
||||
SPECIAL_START_ID = 151643
|
||||
SPECIAL_TOKENS = tuple(
|
||||
enumerate(
|
||||
(
|
||||
(
|
||||
ENDOFTEXT,
|
||||
IMSTART,
|
||||
IMEND,
|
||||
)
|
||||
+ EXTRAS
|
||||
),
|
||||
start=SPECIAL_START_ID,
|
||||
)
|
||||
)
|
||||
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
|
||||
|
||||
|
||||
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
||||
with open(tiktoken_bpe_file, "rb") as f:
|
||||
contents = f.read()
|
||||
return {
|
||||
base64.b64decode(token): int(rank)
|
||||
for token, rank in (line.split() for line in contents.splitlines() if line)
|
||||
}
|
||||
|
||||
|
||||
class QWenTokenizer(PreTrainedTokenizer):
|
||||
"""QWen tokenizer."""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
errors="replace",
|
||||
extra_vocab_file=None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# how to handle errors in decoding UTF-8 byte sequences
|
||||
# use ignore if you are in streaming inference
|
||||
self.errors = errors
|
||||
|
||||
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
|
||||
self.special_tokens = {
|
||||
token: index
|
||||
for index, token in SPECIAL_TOKENS
|
||||
}
|
||||
|
||||
# try load extra vocab from file
|
||||
if extra_vocab_file is not None:
|
||||
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
|
||||
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
|
||||
for token, index in extra_mergeable_ranks.items():
|
||||
if token in self.mergeable_ranks:
|
||||
logger.info(f"extra token {token} exists, skipping")
|
||||
continue
|
||||
if index in used_ids:
|
||||
logger.info(f'the index {index} for extra token {token} exists, skipping')
|
||||
continue
|
||||
self.mergeable_ranks[token] = index
|
||||
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
|
||||
|
||||
enc = tiktoken.Encoding(
|
||||
"Qwen",
|
||||
pat_str=PAT_STR,
|
||||
mergeable_ranks=self.mergeable_ranks,
|
||||
special_tokens=self.special_tokens,
|
||||
)
|
||||
assert (
|
||||
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
||||
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
||||
|
||||
self.decoder = {
|
||||
v: k for k, v in self.mergeable_ranks.items()
|
||||
} # type: dict[int, bytes|str]
|
||||
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
||||
|
||||
self.tokenizer = enc # type: tiktoken.Encoding
|
||||
|
||||
self.eod_id = self.tokenizer.eot_token
|
||||
self.im_start_id = self.special_tokens[IMSTART]
|
||||
self.im_end_id = self.special_tokens[IMEND]
|
||||
|
||||
def __getstate__(self):
|
||||
# for pickle lovers
|
||||
state = self.__dict__.copy()
|
||||
del state["tokenizer"]
|
||||
return state
|
||||
|
||||
def __setstate__(self, state):
|
||||
# tokenizer is not python native; don't pass it; rebuild it
|
||||
self.__dict__.update(state)
|
||||
enc = tiktoken.Encoding(
|
||||
"Qwen",
|
||||
pat_str=PAT_STR,
|
||||
mergeable_ranks=self.mergeable_ranks,
|
||||
special_tokens=self.special_tokens,
|
||||
)
|
||||
self.tokenizer = enc
|
||||
|
||||
def __len__(self) -> int:
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def get_vocab(self) -> Dict[bytes, int]:
|
||||
return self.mergeable_ranks
|
||||
|
||||
def convert_tokens_to_ids(
|
||||
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
||||
) -> List[int]:
|
||||
ids = []
|
||||
if isinstance(tokens, (str, bytes)):
|
||||
if tokens in self.special_tokens:
|
||||
return self.special_tokens[tokens]
|
||||
else:
|
||||
return self.mergeable_ranks.get(tokens)
|
||||
for token in tokens:
|
||||
if token in self.special_tokens:
|
||||
ids.append(self.special_tokens[token])
|
||||
else:
|
||||
ids.append(self.mergeable_ranks.get(token))
|
||||
return ids
|
||||
|
||||
def _add_tokens(
|
||||
self,
|
||||
new_tokens: Union[List[str], List[AddedToken]],
|
||||
special_tokens: bool = False,
|
||||
) -> int:
|
||||
if not special_tokens and new_tokens:
|
||||
raise ValueError("Adding regular tokens is not supported")
|
||||
for token in new_tokens:
|
||||
surface_form = token.content if isinstance(token, AddedToken) else token
|
||||
if surface_form not in SPECIAL_TOKENS_SET:
|
||||
raise ValueError("Adding unknown special tokens is not supported")
|
||||
return 0
|
||||
|
||||
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
||||
"""
|
||||
Save only the vocabulary of the tokenizer (vocabulary).
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
file_path = os.path.join(save_directory, "qwen.tiktoken")
|
||||
with open(file_path, "w", encoding="utf8") as w:
|
||||
for k, v in self.mergeable_ranks.items():
|
||||
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
||||
w.write(line)
|
||||
return (file_path,)
|
||||
|
||||
def tokenize(
|
||||
self,
|
||||
text: str,
|
||||
allowed_special: Union[Set, str] = "all",
|
||||
disallowed_special: Union[Collection, str] = (),
|
||||
**kwargs,
|
||||
) -> List[Union[bytes, str]]:
|
||||
"""
|
||||
Converts a string in a sequence of tokens.
|
||||
|
||||
Args:
|
||||
text (`str`):
|
||||
The sequence to be encoded.
|
||||
allowed_special (`Literal["all"]` or `set`):
|
||||
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
||||
Default to "all".
|
||||
disallowed_special (`Literal["all"]` or `Collection`):
|
||||
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
||||
Default to an empty tuple.
|
||||
|
||||
kwargs (additional keyword arguments, *optional*):
|
||||
Will be passed to the underlying model specific encode method.
|
||||
|
||||
Returns:
|
||||
`List[bytes|str]`: The list of tokens.
|
||||
"""
|
||||
tokens = []
|
||||
text = unicodedata.normalize("NFC", text)
|
||||
|
||||
# this implementation takes a detour: text -> token id -> token surface forms
|
||||
for t in self.tokenizer.encode(
|
||||
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
||||
):
|
||||
tokens.append(self.decoder[t])
|
||||
return tokens
|
||||
|
||||
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
||||
"""
|
||||
Converts a sequence of tokens in a single string.
|
||||
"""
|
||||
text = ""
|
||||
temp = b""
|
||||
for t in tokens:
|
||||
if isinstance(t, str):
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
temp = b""
|
||||
text += t
|
||||
elif isinstance(t, bytes):
|
||||
temp += t
|
||||
else:
|
||||
raise TypeError("token should only be of type types or str")
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
return text
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
||||
"""Converts an id to a token, special tokens included"""
|
||||
if index in self.decoder:
|
||||
return self.decoder[index]
|
||||
raise ValueError("unknown ids")
|
||||
|
||||
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
||||
"""Converts a token to an id using the vocab, special tokens included"""
|
||||
if token in self.special_tokens:
|
||||
return self.special_tokens[token]
|
||||
if token in self.mergeable_ranks:
|
||||
return self.mergeable_ranks[token]
|
||||
raise ValueError("unknown token")
|
||||
|
||||
def _tokenize(self, text: str, **kwargs):
|
||||
"""
|
||||
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
||||
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
||||
|
||||
Do NOT take care of added tokens.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def _decode(
|
||||
self,
|
||||
token_ids: Union[int, List[int]],
|
||||
skip_special_tokens: bool = False,
|
||||
errors: str = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
if isinstance(token_ids, int):
|
||||
token_ids = [token_ids]
|
||||
if skip_special_tokens:
|
||||
token_ids = [i for i in token_ids if i < self.eod_id]
|
||||
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
||||
14
tokenizer_config.json
Normal file
14
tokenizer_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"added_tokens_decoder": {},
|
||||
"auto_map": {
|
||||
"AutoTokenizer": [
|
||||
"tokenization_qwen.QWenTokenizer",
|
||||
null
|
||||
]
|
||||
},
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"model_max_length": 8192,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"tokenizer_class": "QWenTokenizer",
|
||||
"use_fast": false
|
||||
}
|
||||
Reference in New Issue
Block a user