Spark-Chemistry-X1-13B

Model Card for iFLYTEK Spark Chemistry-X1-13B

Model Introduction

iFLYTEK Spark Chemistry-X1-13B is a chemistry-specialized large language model developed by the iFLYTEK team. Fine-tuned from the iFLYTEK Spark-X1 foundation model on diverse chemical task datasets, it demonstrates exceptional proficiency in solving complex chemical problems while maintaining strong general capabilities. The model achieves strong performance across chemistry-related benchmarks and shows clear advantages over leading general-purpose models on most evaluation metrics.

Key Features

• Deep Reasoning Architecture: Unified framework combining long Chain-of-Thought (CoT) with dual-process theory, supporting both fast (reactive) and slow (deliberative) thinking modes

• Hybrid Training Stability: Novel attention masking mechanisms decouple training phases for different reasoning modes, preventing interference between data distributions

• Chemical Domain Enhancement: Multi-stage optimization for specialized tasks including:

  • Advanced knowledge Q&A
  • Chemical name conversion
  • Molecular property prediction

Model Summary

Evaluation Results

*Bold = Global SOTA

Evaluation Notes:

1. All results show zero-shot performance averages
2. Consistent evaluation protocol applied across all models
3. DeepSeek-R1, Gemini 2.5 Pro, GPT-4.1, and O3-mini were evaluated using Chain-of-Thought (CoT) reasoning with API verification
4. Spark Chemistry-X1-13B was evaluated using Chain-of-Thought (CoT) reasoning in a local environment on NVIDIA A800 80GB GPUs
5. The evaluation dataset was self-constructed

Usage

requirement

cd /path/to/Spark-Chemistry-X1-13B
# We recommend using Python 3.10
pip install -r requirements.txt
pip install .

Quickstart

from modelscope import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "iflytek/Spark-Chemistry-X1-13B"
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float32,
    device_map="auto",
    trust_remote_code=True
)
# Reactive
chat_history = [
  {
    "role" : "user",
    "content" : "请回答下列问题:高分子材料是否具有柔顺性主要决定于()的运动能力。\nA、主链链节\nB、侧基\nC、侧基内的官能团或原子?"
  }]

inputs = tokenizer.apply_chat_template(
    chat_history,
    tokenize=True,
    return_tensors="pt",
    add_generation_prompt=True
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=8192,
    top_k=1,
    do_sample=True,
    repetition_penalty=1.02,
    temperature=0.7,
    eos_token_id=5,
    pad_token_id=0,
)

response = tokenizer.decode(
    outputs[0][inputs.shape[1] :],
    skip_special_tokens=True
)
print(response)

from modelscope import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "iflytek/Spark-Chemistry-X1-13B"
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float32,
    device_map="auto",
    trust_remote_code=True
)
# Deliberative
chat_history = [
  {
    "role" : "system",
    "content" : "请你先深入剖析给出问题的关键要点与内在逻辑,生成思考过程,再根据思考过程回答给出问题。思考过程以<unused6>开头,在结尾处用<unused7>标注结束,<unused7>后为基于思考过程的回答内容"
  }
  ,
  {
    "role" : "user",
    "content" : "请回答下列问题:高分子材料是否具有柔顺性主要决定于()的运动能力。\nA、主链链节\nB、侧基\nC、侧基内的官能团或原子?"
  }]


inputs = tokenizer.apply_chat_template(
    chat_history,
    tokenize=True,
    return_tensors="pt",
    add_generation_prompt=True
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=8192,
    top_k=1,
    do_sample=True,
    repetition_penalty=1.02,
    temperature=0.7,
    eos_token_id=5,
    pad_token_id=0,
)

response = tokenizer.decode(
    outputs[0][inputs.shape[1] :],
    skip_special_tokens=True
)
print(response)

Optional: Convert FP32 Weights to BF16

The released weights of Spark Chemistry-X1-13B are stored in FP32 precision. For inference efficiency, users can optionally convert the weights into bfloat16 (BF16) format.

from modelscope import AutoModelForCausalLM
import torch

model_name = " /path_to/Spark-Chemistry-X1-13B"

# Load FP32 weights
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float32, # explicitly FP32
    device_map="auto",
    trust_remote_code=True
)

# Convert to BF16
model = model.to(torch.bfloat16)

#  Save BF16 weights for later fast loading
save_path = "./Spark-Chemistry-X1-13B-bf16"
model.save_pretrained(save_path, safe_serialization=True)

License Agreement

iFLYTEK Spark Chemistry-X1-13B is licensed under Apache 2.0.