| | --- |
| | library_name: transformers |
| | base_model: |
| | - zai-org/GLM-4.7-Flash |
| | --- |
| | |
| | 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). |
| |
|
| | ### Example usage: |
| |
|
| | - vLLM |
| |
|
| | ```bash |
| | # Multi-token prediction is supported |
| | model_id=tiny-random/glm-4.7-flash |
| | 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-4.7-flash |
| | 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 |
| | |
| | # Load model and tokenizer |
| | model_id = "tiny-random/glm-4.7-flash" |
| | messages = [{"role": "user", "content": "hello"}] |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | inputs = tokenizer.apply_chat_template( |
| | messages, |
| | tokenize=True, |
| | add_generation_prompt=True, |
| | return_dict=True, |
| | return_tensors="pt", |
| | ) |
| | model = AutoModelForCausalLM.from_pretrained( |
| | pretrained_model_name_or_path=model_id, |
| | torch_dtype=torch.bfloat16, |
| | device_map="cuda", |
| | ) |
| | inputs = inputs.to(model.device) |
| | generated_ids = model.generate( |
| | **inputs, max_new_tokens=32, do_sample=False) |
| | output_text = tokenizer.decode( |
| | generated_ids[0][inputs.input_ids.shape[1]:]) |
| | print(output_text) |
| | ``` |
| |
|
| | ### Codes to create this repo: |
| |
|
| | ```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-4.7-Flash" |
| | save_folder = "/tmp/tiny-random/glm-4.7-flash" |
| | |
| | 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 = json.load(f) |
| | config_json.update({ |
| | 'kv_lora_rank': 384, |
| | 'num_key_value_heads': 1, |
| | 'q_lora_rank': 32, |
| | 'qk_nope_head_dim': 64, |
| | 'qk_rope_head_dim': 192, |
| | 'v_head_dim': 64, |
| | 'num_key_value_heads': 4, |
| | 'num_attention_heads': 4, |
| | }) |
| | config_json['hidden_size'] = 8 |
| | config_json['intermediate_size'] = 32 |
| | config_json['moe_intermediate_size'] = 32 |
| | config_json['num_hidden_layers'] = 2 |
| | config_json['tie_word_embeddings'] = False |
| | config_json['use_cache'] = 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) |
| | with torch.no_grad(): |
| | for name, p in sorted(model.named_parameters()): |
| | torch.nn.init.normal_(p, 0, 0.1) |
| | print(name, p.shape) |
| | # MTP |
| | set_seed(42) |
| | model.model.layers.append(nn.ModuleDict(dict( |
| | embed_tokens=deepcopy(model.model.embed_tokens), |
| | shared_head=nn.ModuleDict(dict( |
| | norm=nn.RMSNorm(config.hidden_size), |
| | head=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) |
| | ``` |
| |
|
| | ### Printing the model: |
| |
|
| | ```text |
| | Glm4MoeLiteForCausalLM( |
| | (model): Glm4MoeLiteModel( |
| | (embed_tokens): Embedding(154880, 8, padding_idx=154820) |
| | (layers): ModuleList( |
| | (0): Glm4MoeLiteDecoderLayer( |
| | (self_attn): Glm4MoeLiteAttention( |
| | (q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (q_a_layernorm): Glm4MoeLiteRMSNorm((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): Glm4MoeLiteRMSNorm((384,), eps=1e-06) |
| | (kv_b_proj): Linear(in_features=384, out_features=512, bias=False) |
| | (o_proj): Linear(in_features=256, out_features=8, bias=False) |
| | ) |
| | (mlp): Glm4MoeLiteMLP( |
| | (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): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
| | (post_attention_layernorm): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
| | ) |
| | (1): Glm4MoeLiteDecoderLayer( |
| | (self_attn): Glm4MoeLiteAttention( |
| | (q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (q_a_layernorm): Glm4MoeLiteRMSNorm((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): Glm4MoeLiteRMSNorm((384,), eps=1e-06) |
| | (kv_b_proj): Linear(in_features=384, out_features=512, bias=False) |
| | (o_proj): Linear(in_features=256, out_features=8, bias=False) |
| | ) |
| | (mlp): Glm4MoeLiteMoE( |
| | (experts): Glm4MoeLiteNaiveMoe( |
| | (act_fn): SiLUActivation() |
| | ) |
| | (gate): Glm4MoeLiteTopkRouter() |
| | (shared_experts): Glm4MoeLiteMLP( |
| | (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): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
| | (post_attention_layernorm): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
| | ) |
| | (2): ModuleDict( |
| | (embed_tokens): Embedding(154880, 8, padding_idx=154820) |
| | (shared_head): ModuleDict( |
| | (norm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | (head): Embedding(154880, 8, padding_idx=154820) |
| | ) |
| | (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): Glm4MoeLiteAttention( |
| | (q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
| | (q_a_layernorm): Glm4MoeLiteRMSNorm((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): Glm4MoeLiteRMSNorm((384,), eps=1e-06) |
| | (kv_b_proj): Linear(in_features=384, out_features=512, bias=False) |
| | (o_proj): Linear(in_features=256, out_features=8, bias=False) |
| | ) |
| | (mlp): Glm4MoeLiteMoE( |
| | (experts): Glm4MoeLiteNaiveMoe( |
| | (act_fn): SiLUActivation() |
| | ) |
| | (gate): Glm4MoeLiteTopkRouter() |
| | (shared_experts): Glm4MoeLiteMLP( |
| | (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): Glm4MoeLiteRMSNorm((8,), eps=1e-05) |
| | (rotary_emb): Glm4MoeLiteRotaryEmbedding() |
| | ) |
| | (lm_head): Linear(in_features=8, out_features=154880, bias=False) |
| | ) |
| | ``` |
