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