qwen2.5-1.5b-smolinstruct

---

license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags:

• qwen2.5
• instruction-tuning
• sft

{
  "_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
  "architectures": [
    "Qwen2ForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "hidden_act": "silu",
  "hidden_size": 1536,
  "initializer_range": 0.02,
  "intermediate_size": 8960,
  "max_position_embeddings": 32768,
  "max_window_layers": 21,
  "model_type": "qwen2",
  "num_attention_heads": 12,
  "num_hidden_layers": 28,
  "num_key_value_heads": 2,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "tie_word_embeddings": true,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.45.2",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 151936
}

使用方法

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "AlexanderWang915/qwen2.5-1.5b-smolinstruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

# 示例对话
messages = [
    {"role": "user", "content": "你好！"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95
)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

训练详情

• 使用 LLaMA-Factory 进行训练
• 基于 SmolInstruct 数据集的100k样本
• 指令微调以提升对话和指令遵循能力

注意事项

本模型继承了基础模型的许可协议。使用时请遵守相关法律法规，不得用于违法违规用途。