commit 99f42b03e6eb37da47cc602797664819cd3ba899 Author: ModelHub XC Date: Thu Jun 4 13:01:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: HuggingFaceTB/SmolLM3-3B-GSM8K-SFT Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..7b591ff --- /dev/null +++ b/README.md @@ -0,0 +1,162 @@ +--- +license: apache-2.0 +base_model: HuggingFaceTB/SmolLM3-3B-Base +tags: +- math +- gsm8k +- sft +- fine-tuned +- reasoning +datasets: +- meta-math/MetaMathQA +language: +- en +pipeline_tag: text-generation +metrics: +- accuracy +model-index: +- name: SmolLM3-3B-GSM8K-SFT + results: + - task: + type: text-generation + name: Math Reasoning + dataset: + name: GSM8K + type: openai/gsm8k + metrics: + - type: accuracy + value: 65.8 + name: GSM8K Accuracy +--- + +# SmolLM3-3B-GSM8K-SFT + +Fine-tuned version of [HuggingFaceTB/SmolLM3-3B-Base](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base) optimized for grade school math (GSM8K benchmark). + +## Performance + +| Metric | Score | +|--------|-------| +| **GSM8K Accuracy** | **65.8%** | +| Baseline (SmolLM3-3B-Base) | 23.3% | +| **Improvement** | **+42.5 pp (2.8x)** | + +## Quick Start + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_id = "HuggingFaceTB/SmolLM3-3B-GSM8K-SFT" +model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto") +tokenizer = AutoTokenizer.from_pretrained(model_id) + +# Solve a math problem +messages = [{"role": "user", "content": "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?"}] + +inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device) +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False) +print(tokenizer.decode(outputs[0], skip_special_tokens=False)) +``` + +**Expected output:** +``` +Janet's ducks lay 16 eggs per day. +She eats 3 for breakfast, so 16 - 3 = 13 eggs remain. +She bakes muffins with 4 eggs, so 13 - 4 = 9 eggs remain. +She sells the remaining 9 eggs at $2 each. +9 × $2 = $18 + +#### 18 +``` + +## Using with vLLM (Recommended for Speed) + +```python +from vllm import LLM, SamplingParams + +llm = LLM(model="HuggingFaceTB/SmolLM3-3B-GSM8K-SFT") +tokenizer = llm.get_tokenizer() + +messages = [{"role": "user", "content": "What is 15 * 23 + 47?"}] +prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + +outputs = llm.generate([prompt], SamplingParams(max_tokens=256, temperature=0)) +print(outputs[0].outputs[0].text) +``` + +## Training Details + +| Parameter | Value | +|-----------|-------| +| **Base Model** | HuggingFaceTB/SmolLM3-3B-Base | +| **Training Data** | [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) (100k samples) | +| **Method** | Supervised Fine-Tuning (SFT) with TRL 1.0.0 | +| **Hardware** | NVIDIA H100 80GB | +| **Training Time** | ~3h 16min | +| **Epochs** | 1 | +| **Batch Size** | 2 (effective 16 with gradient accumulation) | +| **Learning Rate** | 1e-5 | +| **Max Sequence Length** | 2048 | +| **Optimizer** | AdamW | + +## Chat Template + +This model uses the ChatML format: + +``` +<|im_start|>system +You are a helpful AI assistant.<|im_end|> +<|im_start|>user +What is 2 + 2?<|im_end|> +<|im_start|>assistant +2 + 2 = 4 + +#### 4<|im_end|> +``` + +## Training History + +We tried multiple approaches to improve math reasoning: + +| Stage | GSM8K Accuracy | Method | Notes | +|-------|----------------|--------|-------| +| Baseline | 23.3% | - | SmolLM3-3B-Base with no training | +| SFT V1 | 59.6% | SFT 2 epochs | MetaMathQA 50k samples | +| GRPO | 58% | GRPO | GSM8K train set - ineffective | +| **SFT V2** | **65.8%** | SFT 1 epoch | MetaMathQA 100k samples ✓ | + +**Key finding:** More diverse training data (100k vs 50k samples) was more effective than more epochs or GRPO reinforcement learning. + +## Reproduction + +Training and evaluation scripts are available in the `training/` folder: + +```bash +# Train from scratch +python training/train_sft.py + +# Evaluate on GSM8K +python training/evaluate_gsm8k.py --model HuggingFaceTB/SmolLM3-3B-GSM8K-SFT --samples 1319 +``` + +## Limitations + +- Optimized specifically for grade school math; may not generalize to advanced mathematics +- Best performance with step-by-step reasoning format ending with `#### answer` +- Context window limited to 2048 tokens during training + +## Citation + +```bibtex +@misc{smollm3-gsm8k-sft, + title={SmolLM3-3B-GSM8K-SFT: Fine-tuned SmolLM3 for Math Reasoning}, + author={Hugging Face}, + year={2026}, + publisher={Hugging Face}, + url={https://huggingface.co/HuggingFaceTB/SmolLM3-3B-GSM8K-SFT} +} +``` + +## License + +Apache 2.0 diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..cf80c0f --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,8 @@ +{% for message in messages %}{% if message['role'] == 'system' %}<|im_start|>system +{{ message['content'] }}<|im_end|> +{% elif message['role'] == 'user' %}<|im_start|>user +{{ message['content'] }}<|im_end|> +{% elif message['role'] == 'assistant' %}<|im_start|>assistant +{{ message['content'] }}<|im_end|> +{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant +{% endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..814b285 --- /dev/null +++ b/config.json @@ -0,0 +1,115 @@ +{ + 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"model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|end_of_text|>", + "tokenizer_class": "PreTrainedTokenizerFast" +} diff --git a/training/evaluate_gsm8k.py b/training/evaluate_gsm8k.py new file mode 100644 index 0000000..055f6dc --- /dev/null +++ b/training/evaluate_gsm8k.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +""" +Simple GSM8K evaluation script for SmolLM3-3B-GSM8K-SFT + +Requirements: + pip install transformers datasets vllm + +Usage: + python evaluate_gsm8k.py --model HuggingFaceTB/SmolLM3-3B-GSM8K-SFT --samples 100 +""" + +import argparse +import re +from datasets import load_dataset +from vllm import LLM, SamplingParams + +def extract_answer(text: str) -> str: + """Extract numerical answer from model output.""" + # Look for #### followed by number + match = re.search(r'####\s*(-?[\d,]+(?:\.\d+)?)', text) + if match: + return match.group(1).replace(',', '') + + # Look for "answer is X" pattern + match = re.search(r'answer is[:\s]*(-?[\d,]+(?:\.\d+)?)', text.lower()) + if match: + return match.group(1).replace(',', '') + + # Extract last number + numbers = re.findall(r'-?[\d,]+(?:\.\d+)?', text) + if numbers: + return numbers[-1].replace(',', '') + + return "" + +def extract_gold_answer(text: str) -> str: + """Extract gold answer from GSM8K format.""" + match = re.search(r'####\s*(-?[\d,]+(?:\.\d+)?)', text) + if match: + return match.group(1).replace(',', '') + return "" + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, default="HuggingFaceTB/SmolLM3-3B-GSM8K-SFT") + parser.add_argument("--samples", type=int, default=100) + args = parser.parse_args() + + print(f"Loading model: {args.model}") + llm = LLM( + model=args.model, + trust_remote_code=True, + gpu_memory_utilization=0.9, + ) + + tokenizer = llm.get_tokenizer() + sampling_params = SamplingParams( + max_tokens=512, + temperature=0.0, + stop=["<|im_end|>"], + ) + + print("Loading GSM8K test set...") + dataset = load_dataset("openai/gsm8k", "main", split="test") + dataset = dataset.select(range(min(args.samples, len(dataset)))) + + # Prepare prompts + prompts = [] + for example in dataset: + messages = [{"role": "user", "content": example["question"]}] + prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + prompts.append(prompt) + + # Generate + print(f"Evaluating {len(prompts)} samples...") + outputs = llm.generate(prompts, sampling_params) + + # Score + correct = 0 + for i, (example, output) in enumerate(zip(dataset, outputs)): + pred = extract_answer(output.outputs[0].text) + gold = extract_gold_answer(example["answer"]) + + if pred == gold: + correct += 1 + + accuracy = correct / len(dataset) * 100 + print(f"\nResults:") + print(f" Correct: {correct}/{len(dataset)}") + print(f" Accuracy: {accuracy:.1f}%") + +if __name__ == "__main__": + main() diff --git a/training/requirements.txt b/training/requirements.txt new file mode 100644 index 0000000..5b0df35 --- /dev/null +++ b/training/requirements.txt @@ -0,0 +1,8 @@ +torch>=2.0.0 +transformers>=4.40.0 +trl>=1.0.0 +datasets>=2.18.0 +accelerate>=0.27.0 +vllm>=0.4.0 +flash-attn>=2.5.0 +bitsandbytes>=0.42.0 diff --git a/training/train_sft.py b/training/train_sft.py new file mode 100644 index 0000000..f50f438 --- /dev/null +++ b/training/train_sft.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +""" +SFT training script for SmolLM3-3B-GSM8K-SFT +Fine-tunes SmolLM3-3B-Base on MetaMathQA for math reasoning. + +Requirements: + pip install transformers trl datasets torch + +Usage: + python train_sft.py +""" + +import torch +from datasets import load_dataset +from transformers import AutoModelForCausalLM, AutoTokenizer +from trl import SFTConfig, SFTTrainer + +# Configuration +BASE_MODEL = "HuggingFaceTB/SmolLM3-3B-Base" +OUTPUT_DIR = "./sft_output" +FINAL_MODEL_DIR = "./final_model" +NUM_SAMPLES = 100000 # Use 100k samples from MetaMathQA + +# ChatML template for base model (it has no chat template by default) +CHAT_TEMPLATE = """{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}""" + +def format_example(example): + """Format the MetaMathQA example into conversation format.""" + question = example['query'] + answer = example['response'] + + text = f"""<|im_start|>system +You are a helpful AI assistant that solves math problems step by step. Show your work clearly and end with the final numerical answer after ####.<|im_end|> +<|im_start|>user +{question}<|im_end|> +<|im_start|>assistant +{answer}<|im_end|>""" + + return {"text": text} + +def main(): + print("=" * 50) + print("Starting SFT Training for Math Reasoning") + print("=" * 50) + + # Load tokenizer and add chat template + print("\nLoading tokenizer...") + tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + # Add ChatML special tokens + special_tokens = { + "additional_special_tokens": ["<|im_start|>", "<|im_end|>"] + } + tokenizer.add_special_tokens(special_tokens) + tokenizer.chat_template = CHAT_TEMPLATE + + # Load model + print("\nLoading model...") + model = AutoModelForCausalLM.from_pretrained( + BASE_MODEL, + torch_dtype=torch.bfloat16, + attn_implementation="flash_attention_2", + device_map="auto", + ) + + # Resize embeddings for new tokens + model.resize_token_embeddings(len(tokenizer)) + + # Load and prepare dataset + print("\nLoading MetaMathQA dataset...") + dataset = load_dataset("meta-math/MetaMathQA", split="train") + dataset = dataset.shuffle(seed=42).select(range(min(NUM_SAMPLES, len(dataset)))) + print(f"Using {len(dataset)} samples") + + # Format dataset + print("\nFormatting dataset...") + formatted_dataset = dataset.map( + format_example, + remove_columns=dataset.column_names + ) + + # Split for train/eval + split_dataset = formatted_dataset.train_test_split(test_size=0.01, seed=42) + train_dataset = split_dataset['train'] + eval_dataset = split_dataset['test'] + + print(f"Train samples: {len(train_dataset)}") + print(f"Eval samples: {len(eval_dataset)}") + + # Training configuration + training_args = SFTConfig( + output_dir=OUTPUT_DIR, + num_train_epochs=1, + per_device_train_batch_size=2, + gradient_accumulation_steps=8, # Effective batch size = 16 + learning_rate=1e-5, + lr_scheduler_type="cosine", + warmup_ratio=0.05, + weight_decay=0.01, + logging_steps=50, + save_steps=500, + save_total_limit=2, + eval_strategy="steps", + eval_steps=500, + bf16=True, + gradient_checkpointing=True, + report_to="none", + max_length=2048, + seed=42, + packing=False, + ) + + # Create trainer + print("\nInitializing SFT Trainer...") + trainer = SFTTrainer( + model=model, + args=training_args, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=tokenizer, + ) + + # Train + print("\nStarting training...") + trainer.train() + + # Save final model + print(f"\nSaving final model to {FINAL_MODEL_DIR}...") + trainer.save_model(FINAL_MODEL_DIR) + tokenizer.save_pretrained(FINAL_MODEL_DIR) + + print("\n" + "=" * 50) + print("Training completed!") + print("=" * 50) + +if __name__ == "__main__": + main() diff --git a/training_args.bin b/training_args.bin new file mode 100644 index 0000000..0a34f8b --- /dev/null +++ b/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00a928c87b198b750003285050886aa0c72c601fd48924723197a3bdb7df3b3f +size 6289