This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from zai-org/GLM-4.7-Flash.
Example usage:
- vLLM
# 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
# 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
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:
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:
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)
)