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zhangux/
TinyChat-0.1B-sft

Name: TinyChat-0.1B-sft
Availability: InStock

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TinyChat-0.1B-sft

这是sft版本，后续的RLHF版本会基于此模型进行微调。

模型特性

参数量: 约 0.1B (112M)
架构: Decoder-only Transformer
注意力机制: Grouped Query Attention (GQA)
位置编码: Rotary Position Embedding (RoPE)
归一化: RMSNorm
激活函数: SiLU (Swish)
词汇表大小: 6,400
最大序列长度: 512 tokens

模型配置

- Layers: 12
- Hidden Size: 768
- Attention Heads: 16 (Query) / 4 (Key-Value)
- FFN Hidden Size: 3,072
- Dropout: 0.1

使用方法

安装依赖

pip install torch==2.4.0 transformers==4.46.3

使用示例

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 加载模型和分词器
model = AutoModelForCausalLM.from_pretrained(
    "zhangux/TinyChat-0.1B-sft",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "zhangux/TinyChat-0.1B-sft"
)

prompt = "今天天气怎么样？"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )

inputs = tokenizer(text, return_tensors="pt")
input_ids = inputs.input_ids
output_ids = model.generate(
        input_ids=input_ids,
        do_sample=True,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
        use_cache=True # 如果这里出现了 kv_cache 相关的错误，尝试 use_cache=False
    )

generated_ids = output_ids[0][len(input_ids[0]):]
response = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(response)

参考结果

今天的天气状况：晴朗，无云，无雨，风力较小。建议出门时带上太阳镜和防晒霜。

zhangux/TinyChat-0.1B-sft