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library_name: transformers
pipeline_tag: text-generation
base_model: LiquidAI/LFM2.5-350M
tags:
- causal-lm
- sft
- math
- chatml
- transformers
---
# Math Curated SFT
This is a full-model SFT checkpoint trained from `LiquidAI/LFM2.5-350M` on
`User01110/math-curated-dataset`.
## Training
- Method: TRL `SFTTrainer`
- Dataset split: `train`
- Training rows: 39040
- Epochs: 1
- Max sequence length: 1024
- Target style: full generated response
- Format: the base tokenizer chat template via `tokenizer.apply_chat_template`
- System prompt: `You are a math-focused assistant. Solve the user's math problem and follow the training format: Understanding Query, Drafting Answer, Refining The Answer, and Final Response.`
## Format
Each row is formatted with:
```python
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": prompt},
]
prompt_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
training_text = prompt_text + response + (tokenizer.eos_token or "")
```
## Important limitation
This model is trained on generated math-style data. Responses may contain
incorrect arithmetic or flawed reasoning, and should not be treated as reliable
mathematical answers without independent verification.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "User01110/LFM-2.5-350M-MathMini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a math-focused assistant. Solve the user's math problem and follow the training format: Understanding Query, Drafting Answer, Refining The Answer, and Final Response."},
{"role": "user", "content": "John has 22 apples, he eats 10 of them, how many apples does john have now?"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=False,
repetition_penalty=1.1,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```
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