---
library_name: transformers
base_model:
- zai-org/GLM-5.1
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.1).
| File path | Size |
|------|------|
| model.safetensors | 17.6MB |
### Example usage:
- vLLM
```bash
# Multi-token prediction is supported
model_id=tiny-random/glm-5.1
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-5.1
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-5.1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(model_id)
input_ids = torch.randint(1000, 2000, size=(1, 2333), dtype=torch.long).to(device) # trigger DSA
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype=torch.bfloat16,
device_map=device,
)
generated_ids = model.generate(input_ids, max_new_tokens=8)
output_text = tokenizer.decode(generated_ids[0][input_ids.shape[1]:])
print(output_text)
```
### Codes to create this repo:
Click to expand
```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-5.1"
save_folder = "/tmp/tiny-random/glm-51"
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)
config_json.update({
"first_k_dense_replace": 1,
"mlp_layer_types": ['dense'] + ['sparse'],
"hidden_size": 8,
"index_n_heads": 4,
"intermediate_size": 32,
"moe_intermediate_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 8,
'num_key_value_heads': 8,
'q_lora_rank': 32,
'tie_word_embeddings': False,
})
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, dtype=torch.bfloat16)
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, 0.2)
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)
```
### Printing the model:
Click to expand
```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=2048, 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=3584, bias=False)
(o_proj): Linear(in_features=2048, out_features=8, bias=False)
(indexer): GlmMoeDsaIndexer(
(wq_b): Linear(in_features=32, out_features=512, bias=False)
(wk): Linear(in_features=8, out_features=128, bias=False)
(k_norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(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=2048, 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=3584, bias=False)
(o_proj): Linear(in_features=2048, out_features=8, bias=False)
(indexer): GlmMoeDsaIndexer(
(wq_b): Linear(in_features=32, out_features=512, bias=False)
(wk): Linear(in_features=8, out_features=128, bias=False)
(k_norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(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=2048, 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=3584, bias=False)
(o_proj): Linear(in_features=2048, out_features=8, bias=False)
(indexer): GlmMoeDsaIndexer(
(wq_b): Linear(in_features=32, out_features=512, bias=False)
(wk): Linear(in_features=8, out_features=128, bias=False)
(k_norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(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)
)
```