---
library_name: transformers
license: other
base_model: Qwen2.5-Math-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: SocraticLM
  results: []
language:
- en
- zh
pipeline_tag: text-generation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

## Model description

This model is a fine-tuned version of [Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the [SocraTeach dataset](https://github.com/Ljyustc/SocraticLM).

It is an implementation of [SocraticLM](https://github.com/Ljyustc/SocraticLM).

## Intended uses & limitations

[SocraticLM](https://github.com/Ljyustc/SocraticLM) is designed for educational perposes, where students need a Socratic-style guidance when having difficulties learning to solve mathematical problems.
Also, [SocraticLM](https://github.com/Ljyustc/SocraticLM) can solve mathematical problems itself.

This model mainly supports English and Chinese.

## How to use

For Huggingface transformers:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CogBase-USTC/SocraticLM")
model = AutoModelForCausalLM.from_pretrained(
    "CogBase-USTC/SocraticLM",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

### Math Problem Solving ###
messages = [
    {"role": "system", "content" : "Please analyse and solve the following problem step by step."},
    {"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"},
]

### Socratic-style Guidance ###
# messages = [
#     {"role": "system", "content" : "You are a Socratic teacher, please guide me to solve the [Problem] with heuristic questions based on the following information. \n"},
#     {"role": "user", "content": "[Problem] Debelyn, Christel, and Andrena collect dolls. Debelyn had 20 dolls before she gave Andrena 2 dolls. Christel had 24 dolls before giving Andrena 5 dolls. After all the gifts, Andrena now has 2 more dolls  than Christel, how many more dolls does andrena have now than Debelyn? [Answer] 3 [Analysis] Debelyn had 20 - 2 = 18 dolls left after giving out 2 dolls to Christel. Christel had 24 + 2 = 26 dolls after receiving 2 dolls from Debelyn. Christel had 24 - 5 = 19 dolls after giving Andrena 5 dolls. So, Andrena has 19 +2 = 21 dolls now. Therefore, Andrena has 21 - 18 = 3 more dolls than Debelyn."},
# ]

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

inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=4096)
print(tokenizer.decode(outputs[0]))
```

For vLLM:
```python
from vllm import LLM, SamplingParams

llm = LLM(model=r'CogBase-USTC/SocraticLM',
          tokenizer=r'CogBase-USTC/SocraticLM',
          trust_remote_code=True,
          tensor_parallel_size=1,
          gpu_memory_utilization=0.99,
          enable_chunked_prefill=True,
          max_num_batched_tokens=512,
          max_num_seqs=128)
sampling_params = SamplingParams(temperature=0, max_tokens=4096, seed=42)


def print_outputs(outputs):
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Generated text: {generated_text!r}")
    print("-" * 80)


print("=" * 80)

### Math Problem Solving ###
conversation = [
    {
        "role": "system",
        "content": "Please analyse and solve the following problem step by step."
    },
    {
        "role": "user", 
        "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"
    },
]

### Socratic-style Guidance ###
# conversation = [
#     {
#         "role": "system",
#         "content": "You are a Socratic teacher, please guide me to solve the [Problem] with heuristic questions based on the following information. \n"
#     },
#     {
#         "role": "user", 
#         "content": "[Problem] Debelyn, Christel, and Andrena collect dolls. Debelyn had 20 dolls before she gave Andrena 2 dolls. Christel had 24 dolls before giving Andrena 5 dolls. After all the gifts, Andrena now has 2 more dolls  than Christel, how many more dolls does andrena have now than Debelyn? [Answer] 3 [Analysis] Debelyn had 20 - 2 = 18 dolls left after giving out 2 dolls to Christel. Christel had 24 + 2 = 26 dolls after receiving 2 dolls from Debelyn. Christel had 24 - 5 = 19 dolls after giving Andrena 5 dolls. So, Andrena has 19 +2 = 21 dolls now. Therefore, Andrena has 21 - 18 = 3 more dolls than Debelyn."
#     },
# ]

outputs = llm.chat(conversation,
                   sampling_params=sampling_params,
                   use_tqdm=False,
                )
print_outputs(outputs)
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20

### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3