This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from tencent/Hy3.
| File path | Size |
|---|---|
| model.safetensors | 5.4MB |
Example usage:
- vLLM
# Multi-token prediction is supported
model_id=yujiepan/hy3-tiny-random
vllm serve $model_id \
--tensor-parallel-size 2 \
--speculative-config.method mtp \
--speculative-config.num_speculative_tokens 1 \
--tool-call-parser hy_v3 \
--reasoning-parser hy_v3 \
--enable-auto-tool-choice
- SGLang
# Multi-token prediction is supported
model_id=yujiepan/hy3-tiny-random
python3 -m sglang.launch_server \
--model $model_id \
--tp 2 \
--tool-call-parser hunyuan \
--reasoning-parser hunyuan \
--speculative-num-steps 1 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 2 \
--speculative-algorithm EAGLE
- Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "yujiepan/hy3-tiny-random"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
messages = [
{"role": "user", "content": "Write a short poem about AI."},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
return_tensors="pt",
add_generation_prompt=True,
reasoning_effort='high',
)
print(inputs)
outputs = model.generate(**inputs.to(model.device), max_new_tokens=32)
output_text = tokenizer.decode(outputs[0])
print(output_text)
Codes to create this repo:
import json
from copy import deepcopy
from pathlib import Path
import torch
import torch.nn as nn
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "tencent/Hy3"
save_folder = "/tmp/yujiepan/hy3-tiny-random"
processor = AutoTokenizer.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({
'expert_hidden_dim': 32,
'moe_intermediate_size': 32,
'head_dim': 32,
'hidden_size': 8,
'intermediate_size': 32,
'num_attention_heads': 8,
'num_hidden_layers': 4,
'num_key_value_heads': 4,
})
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)
set_seed(42)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True).eval().cpu()
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.top_k = 40 # original value in source model is -1 , which is invalid
# mtp
mtp = deepcopy(model.model.layers[-1])
mtp.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
mtp.enorm = nn.RMSNorm(config.hidden_size)
mtp.hnorm = nn.RMSNorm(config.hidden_size)
mtp.final_layernorm = nn.RMSNorm(config.hidden_size)
model.model.layers.append(mtp)
# init weights
set_seed(42)
model = model.cpu().eval()
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.dtype, f'{p.numel() / n_params * 100: .2f}%')
# expert bias is in float32
for i in range(config.first_k_dense_replace, config.num_hidden_layers + 1, 1):
model.model.layers[i].mlp.e_score_correction_bias = nn.Parameter(torch.randn_like(
model.model.layers[i].mlp.e_score_correction_bias
).float() * 0.002)
model.save_pretrained(save_folder)
print(model)
torch.set_default_dtype(torch.float32)
Printing the model:
HYV3ForCausalLM(
(model): HYV3Model(
(embed_tokens): Embedding(120832, 8, padding_idx=120002)
(layers): ModuleList(
(0): HYV3DecoderLayer(
(self_attn): HYV3Attention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): HYV3RMSNorm((32,), eps=1e-05)
(k_norm): HYV3RMSNorm((32,), eps=1e-05)
)
(mlp): HYV3MLP(
(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): HYV3RMSNorm((8,), eps=1e-05)
(post_attention_layernorm): HYV3RMSNorm((8,), eps=1e-05)
)
(1-3): 3 x HYV3DecoderLayer(
(self_attn): HYV3Attention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): HYV3RMSNorm((32,), eps=1e-05)
(k_norm): HYV3RMSNorm((32,), eps=1e-05)
)
(mlp): HYV3MoE(
(gate): HYV3TopKRouter()
(experts): HYV3Experts(
(act_fn): SiLUActivation()
)
(shared_experts): HYV3MLP(
(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): HYV3RMSNorm((8,), eps=1e-05)
(post_attention_layernorm): HYV3RMSNorm((8,), eps=1e-05)
)
(4): HYV3DecoderLayer(
(self_attn): HYV3Attention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): HYV3RMSNorm((32,), eps=1e-05)
(k_norm): HYV3RMSNorm((32,), eps=1e-05)
)
(mlp): HYV3MoE(
(gate): HYV3TopKRouter()
(experts): HYV3Experts(
(act_fn): SiLUActivation()
)
(shared_experts): HYV3MLP(
(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): HYV3RMSNorm((8,), eps=1e-05)
(post_attention_layernorm): HYV3RMSNorm((8,), eps=1e-05)
(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)
(final_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
)
)
(norm): HYV3RMSNorm((8,), eps=1e-05)
(rotary_emb): HYV3RotaryEmbedding()
)
(lm_head): Linear(in_features=8, out_features=120832, bias=False)
)
Test environment:
- torch: 2.11.0+cu128
- transformers: 5.13.0