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

File size: 2,426 Bytes

---
license: mit
library_name: transformers
language:
- zh
tags:
- qwen2
- qwen
- base-model
- pretrained
- chinese
- from-scratch
datasets:
- ddzhu123/seq-monkey
---

# 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](https://www.modelscope.cn/datasets/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）

```python
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))