Instructions to use flylcw/seq_monkey_pretrain_base_1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flylcw/seq_monkey_pretrain_base_1.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="flylcw/seq_monkey_pretrain_base_1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flylcw/seq_monkey_pretrain_base_1.5B")
model = AutoModelForCausalLM.from_pretrained("flylcw/seq_monkey_pretrain_base_1.5B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use flylcw/seq_monkey_pretrain_base_1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flylcw/seq_monkey_pretrain_base_1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flylcw/seq_monkey_pretrain_base_1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/flylcw/seq_monkey_pretrain_base_1.5B

SGLang

How to use flylcw/seq_monkey_pretrain_base_1.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "flylcw/seq_monkey_pretrain_base_1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flylcw/seq_monkey_pretrain_base_1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "flylcw/seq_monkey_pretrain_base_1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flylcw/seq_monkey_pretrain_base_1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use flylcw/seq_monkey_pretrain_base_1.5B with Docker Model Runner:
```
docker model run hf.co/flylcw/seq_monkey_pretrain_base_1.5B
```

seq_monkey_pretrain_base_1.5B（中文 Base 预训练模型）

基于 Qwen2.5-1.5B 架构、在出门问问「序列猴子」中文通用语料上 from-scratch（随机初始化）预训练 的中文 Base 模型。本模型为 Base（续写）模型，未经指令微调，适合下游继续预训练 / SFT，或直接做文本续写。

模型结构

项目	值
架构	Qwen2ForCausalLM（Decoder-only，LLaMA 同族）
参数量	1.5B 级（含 15 万词表 embedding，HF 统计约 2B）
hidden_size	1536
num_hidden_layers	28
num_attention_heads	12
num_key_value_heads	2（GQA）
intermediate_size	8960
vocab_size	151936
max_position_embeddings	131072
激活函数	SiLU（SwiGLU）
归一化	RMSNorm（eps=1e-6）
位置编码	RoPE
精度	BF16

预训练细节

训练方式：from-scratch 随机初始化（非加载官方权重），Causal LM
训练数据：ddzhu123/seq-monkey 的 mobvoi_seq_monkey_general_open_corpus——序列猴子中文通用文本语料，约 1300 万份中文文本，来源涵盖网页、百科、书籍等，许可 Apache-2.0
分词器：Qwen2.5 Tokenizer（vocab 151936）
序列长度：block_size = 1024
优化：bf16 + DeepSpeed ZeRO-2，8×NVIDIA A800-40G
学习率：2e-4，warmup + cosine
训练规模：1 epoch
收敛情况：loss 从 ~12（≈ln(151936)，随机初始化理论起点）平滑收敛至 ~3.x，grad_norm 稳定

快速开始（Base = 续写，不用 chat template）

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

name = "flylcw/seq_monkey_pretrain_base_1.5B"
tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

prompt = "人工智能正在改变"
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=128, do_sample=True,
                     temperature=0.7, top_p=0.9, repetition_penalty=1.1)
print(tok.decode(out[0], skip_special_tokens=True))

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for flylcw/seq_monkey_pretrain_base_1.5B

Finetunes

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