| | --- |
| | library_name: transformers |
| | base_model: |
| | - zai-org/GLM-5 |
| | --- |
| | |
| | This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5). |
| |
|
| | | File path | Size | |
| | |------|------| |
| | | model.safetensors | 9.0MB | |
| |
|
| |
|
| | ### Example usage: |
| |
|
| | - vLLM |
| |
|
| | ```bash |
| | # Multi-token prediction is supported |
| | model_id=tiny-random/glm-5 |
| | vllm serve $model_id \ |
| | --tensor-parallel-size 2 \ |
| | --speculative-config.method mtp \ |
| | --speculative-config.num_speculative_tokens 1 \ |
| | --tool-call-parser glm47 \ |
| | --reasoning-parser glm45 \ |
| | --enable-auto-tool-choice |
| | ``` |
| |
|
| | - SGLang |
| |
|
| | ```bash |
| | # Multi-token prediction is supported |
| | model_id=tiny-random/glm-5 |
| | python3 -m sglang.launch_server --model-path $model_id --tp-size 2 \ |
| | --tool-call-parser glm47 \ |
| | --reasoning-parser glm45 \ |
| | --speculative-algorithm EAGLE \ |
| | --speculative-num-steps 3 \ |
| | --speculative-eagle-topk 1 \ |
| | --speculative-num-draft-tokens 4 |
| | ``` |
| |
|
| | - Transformers |
| |
|
| | ```python |
| | import torch |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | model_id = "tiny-random/glm-5" |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | input_ids = torch.randint(1000, 2000, size=(1, 2333), dtype=torch.long).cuda() # trigger DSA |
| | # messages = [{"role": "user", "content": "hello"}] |
| | # input_ids = tokenizer(messages, return_tensors="pt").input_ids.cuda() |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_id, |
| | dtype=torch.bfloat16, |
| | device_map="cuda", |
| | ) |
| | generated_ids = model.generate(input_ids, max_new_tokens=32) |
| | output_text = tokenizer.decode(generated_ids[0][input_ids.shape[1]:]) |
| | print(output_text) |
| | ``` |
| |
|
| | ### Codes to create this repo: |
| |
|
| | <details> |
| | <summary>Click to expand</summary> |
| |
|
| | ```python |
| | import json |
| | from copy import deepcopy |
| | from pathlib import Path |
| | |
| | import accelerate |
| | import torch |
| | import torch.nn as nn |
| | from huggingface_hub import file_exists, hf_hub_download |
| | from transformers import ( |
| | AutoConfig, |
| | AutoModelForCausalLM, |
| | AutoProcessor, |
| | GenerationConfig, |
| | set_seed, |
| | ) |
| | |
| | source_model_id = "zai-org/GLM-5" |
| | save_folder = "/tmp/tiny-random/glm-5" |
| | |
| | processor = AutoProcessor.from_pretrained( |
| | source_model_id, trust_remote_code=True) |
| | processor.save_pretrained(save_folder) |
| | |
| | with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f: |
| | config_json: dict = json.load(f) |
| | |
| | head_dim = 64 |
| | kv_lora_rank = 512 |
| | qk_nope_head_dim = 192 |
| | config_json.update({ |
| | "first_k_dense_replace": 1, |
| | "mlp_layer_types": ['dense'] + ['sparse'], |
| | "head_dim": head_dim, |
| | "hidden_size": 8, |
| | "index_head_dim": 32, |
| | "index_n_heads": 4, |
| | "intermediate_size": 32, |
| | "moe_intermediate_size": 32, |
| | "num_hidden_layers": 2, |
| | 'kv_lora_rank': kv_lora_rank, |
| | "num_attention_heads": 4, |
| | 'num_key_value_heads': 4, |
| | 'q_lora_rank': 32, |
| | "qk_head_dim": qk_nope_head_dim + head_dim, |
| | 'qk_nope_head_dim': qk_nope_head_dim, |
| | 'qk_rope_head_dim': head_dim, |
| | 'v_head_dim': qk_nope_head_dim + head_dim, |
| | "tie_word_embeddings": True, |
| | }) |
| | with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: |
| | json.dump(config_json, f, indent=2) |
| | |
| | config = AutoConfig.from_pretrained( |
| | save_folder, |
| | trust_remote_code=True, |
| | ) |
| | print(config) |
| | torch.set_default_dtype(torch.bfloat16) |
| | model = AutoModelForCausalLM.from_config(config) |
| | torch.set_default_dtype(torch.float32) |
| | |
| | if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'): |
| | model.generation_config = GenerationConfig.from_pretrained( |
| | source_model_id, trust_remote_code=True, |
| | ) |
| | model.generation_config.do_sample = True |
| | print(model.generation_config) |
| | |
| | model = model.cpu() |
| | set_seed(42) |
| | n_params = sum(p.numel() for p in model.parameters()) |
| | with torch.no_grad(): |
| | for name, p in sorted(model.named_parameters()): |
| | torch.nn.init.normal_(p, 0, 0.1) |
| | print(name, p.shape, p.numel() / n_params * 100, '%') |
| | # MTP |
| | set_seed(42) |
| | model.model.layers.append(nn.ModuleDict(dict( |
| | shared_head=nn.ModuleDict(dict( |
| | norm=nn.RMSNorm(config.hidden_size), |
| | # head=deepcopy(model.model.embed_tokens), |
| | )), |
| | # embed_tokens=deepcopy(model.model.embed_tokens), |
| | eh_proj=nn.Linear(config.hidden_size * 2, |
| | config.hidden_size, bias=False), |
| | enorm=nn.RMSNorm(config.hidden_size), |
| | hnorm=nn.RMSNorm(config.hidden_size), |
| | input_layernorm=nn.RMSNorm(config.hidden_size), |
| | post_attention_layernorm=nn.RMSNorm(config.hidden_size), |
| | self_attn=deepcopy(model.model.layers[1].self_attn), |
| | mlp=deepcopy(model.model.layers[1].mlp), |
| | ))) |
| | for i in range(1, len(model.model.layers)): |
| | model.model.layers[i].mlp.gate.e_score_correction_bias = torch.rand_like( |
| | model.model.layers[i].mlp.gate.e_score_correction_bias).float() |
| | model.save_pretrained(save_folder) |
| | print(model) |
| | ``` |
| |
|
| | </details> |
| |
|
| | ### Printing the model: |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | ```text |
| | GlmMoeDsaForCausalLM( |
| | (model): GlmMoeDsaModel( |
| | (embed_tokens): Embedding(154880, 8, padding_idx=154820) |
| | (layers): ModuleList( |
| | (0): GlmMoeDsaDecoderLayer( |
| | (self_attn): GlmMoeDsaAttention( |
| | (q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (q_a_layernorm): GlmMoeDsaRMSNorm((32,), eps=1e-06) |
| | (q_b_proj): Linear(in_features=32, out_features=1024, bias=False) |
| | (kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False) |
| | (kv_a_layernorm): GlmMoeDsaRMSNorm((512,), eps=1e-06) |
| | (kv_b_proj): Linear(in_features=512, out_features=1792, bias=False) |
| | (o_proj): Linear(in_features=1024, out_features=8, bias=False) |
| | (wq_b): Linear(in_features=32, out_features=1024, bias=False) |
| | (wk): Linear(in_features=8, out_features=256, bias=False) |
| | (k_norm): GlmMoeDsaRMSNorm((256,), eps=1e-06) |
| | (weights_proj): Linear(in_features=8, out_features=4, bias=False) |
| | ) |
| | (mlp): GlmMoeDsaMLP( |
| | (gate_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (up_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (down_proj): Linear(in_features=32, out_features=8, bias=False) |
| | (act_fn): SiLUActivation() |
| | ) |
| | (input_layernorm): GlmMoeDsaRMSNorm((8,), eps=1e-05) |
| | (post_attention_layernorm): GlmMoeDsaRMSNorm((8,), eps=1e-05) |
| | ) |
| | (1): GlmMoeDsaDecoderLayer( |
| | (self_attn): GlmMoeDsaAttention( |
| | (q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (q_a_layernorm): GlmMoeDsaRMSNorm((32,), eps=1e-06) |
| | (q_b_proj): Linear(in_features=32, out_features=1024, bias=False) |
| | (kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False) |
| | (kv_a_layernorm): GlmMoeDsaRMSNorm((512,), eps=1e-06) |
| | (kv_b_proj): Linear(in_features=512, out_features=1792, bias=False) |
| | (o_proj): Linear(in_features=1024, out_features=8, bias=False) |
| | (wq_b): Linear(in_features=32, out_features=1024, bias=False) |
| | (wk): Linear(in_features=8, out_features=256, bias=False) |
| | (k_norm): GlmMoeDsaRMSNorm((256,), eps=1e-06) |
| | (weights_proj): Linear(in_features=8, out_features=4, bias=False) |
| | ) |
| | (mlp): GlmMoeDsaMoE( |
| | (experts): GlmMoeDsaNaiveMoe( |
| | (act_fn): SiLUActivation() |
| | ) |
| | (gate): GlmMoeDsaTopkRouter() |
| | (shared_experts): GlmMoeDsaMLP( |
| | (gate_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (up_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (down_proj): Linear(in_features=32, out_features=8, bias=False) |
| | (act_fn): SiLUActivation() |
| | ) |
| | ) |
| | (input_layernorm): GlmMoeDsaRMSNorm((8,), eps=1e-05) |
| | (post_attention_layernorm): GlmMoeDsaRMSNorm((8,), eps=1e-05) |
| | ) |
| | (2): ModuleDict( |
| | (shared_head): ModuleDict( |
| | (norm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | ) |
| | (eh_proj): Linear(in_features=16, out_features=8, bias=False) |
| | (enorm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | (hnorm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | (input_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | (post_attention_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | (self_attn): GlmMoeDsaAttention( |
| | (q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (q_a_layernorm): GlmMoeDsaRMSNorm((32,), eps=1e-06) |
| | (q_b_proj): Linear(in_features=32, out_features=1024, bias=False) |
| | (kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False) |
| | (kv_a_layernorm): GlmMoeDsaRMSNorm((512,), eps=1e-06) |
| | (kv_b_proj): Linear(in_features=512, out_features=1792, bias=False) |
| | (o_proj): Linear(in_features=1024, out_features=8, bias=False) |
| | (wq_b): Linear(in_features=32, out_features=1024, bias=False) |
| | (wk): Linear(in_features=8, out_features=256, bias=False) |
| | (k_norm): GlmMoeDsaRMSNorm((256,), eps=1e-06) |
| | (weights_proj): Linear(in_features=8, out_features=4, bias=False) |
| | ) |
| | (mlp): GlmMoeDsaMoE( |
| | (experts): GlmMoeDsaNaiveMoe( |
| | (act_fn): SiLUActivation() |
| | ) |
| | (gate): GlmMoeDsaTopkRouter() |
| | (shared_experts): GlmMoeDsaMLP( |
| | (gate_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (up_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (down_proj): Linear(in_features=32, out_features=8, bias=False) |
| | (act_fn): SiLUActivation() |
| | ) |
| | ) |
| | ) |
| | ) |
| | (norm): GlmMoeDsaRMSNorm((8,), eps=1e-05) |
| | (rotary_emb): GlmMoeDsaRotaryEmbedding() |
| | ) |
| | (lm_head): Linear(in_features=8, out_features=154880, bias=False) |
| | ) |
| | ``` |
| |
|
| | </details> |
