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--- |
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library_name: transformers |
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base_model: |
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- zai-org/GLM-4.7-Flash |
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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-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash). |
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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-4.7-flash-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-4.7-flash-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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# Load model and tokenizer |
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model_id = "yujiepan/glm-4.7-flash-tiny-random" |
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messages = [{"role": "user", "content": "hello"}] |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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inputs = tokenizer.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_dict=True, |
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return_tensors="pt", |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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pretrained_model_name_or_path=model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="cuda", |
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) |
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inputs = inputs.to(model.device) |
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generated_ids = model.generate( |
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**inputs, max_new_tokens=32, do_sample=False) |
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output_text = tokenizer.decode( |
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generated_ids[0][inputs.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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```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-4.7-Flash" |
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save_folder = "/tmp/yujiepan/glm-4.7-flash-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 = json.load(f) |
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config_json.update({ |
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'kv_lora_rank': 384, |
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'num_key_value_heads': 1, |
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'q_lora_rank': 32, |
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'qk_nope_head_dim': 64, |
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'qk_rope_head_dim': 192, |
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'v_head_dim': 64, |
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'num_key_value_heads': 4, |
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'num_attention_heads': 4, |
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}) |
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config_json['hidden_size'] = 8 |
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config_json['intermediate_size'] = 32 |
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config_json['moe_intermediate_size'] = 32 |
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config_json['num_hidden_layers'] = 2 |
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config_json['tie_word_embeddings'] = False |
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config_json['use_cache'] = True |
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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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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) |
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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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embed_tokens=deepcopy(model.model.embed_tokens), |
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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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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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### Printing the model: |
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```text |
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Glm4MoeLiteForCausalLM( |
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(model): Glm4MoeLiteModel( |
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(embed_tokens): Embedding(154880, 8, padding_idx=154820) |
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(layers): ModuleList( |
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(0): Glm4MoeLiteDecoderLayer( |
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(self_attn): Glm4MoeLiteAttention( |
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(q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
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(q_a_layernorm): Glm4MoeLiteRMSNorm((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): Glm4MoeLiteRMSNorm((384,), eps=1e-06) |
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(kv_b_proj): Linear(in_features=384, out_features=512, bias=False) |
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(o_proj): Linear(in_features=256, out_features=8, bias=False) |
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) |
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(mlp): Glm4MoeLiteMLP( |
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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): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
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(post_attention_layernorm): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
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) |
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(1): Glm4MoeLiteDecoderLayer( |
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(self_attn): Glm4MoeLiteAttention( |
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(q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
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(q_a_layernorm): Glm4MoeLiteRMSNorm((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): Glm4MoeLiteRMSNorm((384,), eps=1e-06) |
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(kv_b_proj): Linear(in_features=384, out_features=512, bias=False) |
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(o_proj): Linear(in_features=256, out_features=8, bias=False) |
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) |
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(mlp): Glm4MoeLiteMoE( |
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(experts): Glm4MoeLiteNaiveMoe( |
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(act_fn): SiLUActivation() |
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) |
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(gate): Glm4MoeLiteTopkRouter() |
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(shared_experts): Glm4MoeLiteMLP( |
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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): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
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(post_attention_layernorm): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
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) |
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(2): ModuleDict( |
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(embed_tokens): Embedding(154880, 8, padding_idx=154820) |
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(shared_head): ModuleDict( |
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(norm): RMSNorm((8,), eps=None, elementwise_affine=True) |
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(head): Embedding(154880, 8, padding_idx=154820) |
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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): Glm4MoeLiteAttention( |
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(q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
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(q_a_layernorm): Glm4MoeLiteRMSNorm((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): Glm4MoeLiteRMSNorm((384,), eps=1e-06) |
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(kv_b_proj): Linear(in_features=384, out_features=512, bias=False) |
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(o_proj): Linear(in_features=256, out_features=8, bias=False) |
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) |
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(mlp): Glm4MoeLiteMoE( |
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(experts): Glm4MoeLiteNaiveMoe( |
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(act_fn): SiLUActivation() |
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) |
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(gate): Glm4MoeLiteTopkRouter() |
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(shared_experts): Glm4MoeLiteMLP( |
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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): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
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(rotary_emb): Glm4MoeLiteRotaryEmbedding() |
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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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``` |
