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# SmolLM2
端侧小模型新星SmolLM2 1.7B击败Qwen 2.5 1.5B和Llama 3.2 1B，可以在不到2GB的VRAM上运行。
## 论文
`未发表`
## 模型结构
SmolLM2对Llama结构进行了瘦身，认为深-窄结构具有更好的梯度流和参数效率，仍为Decoder-only结构。
<div align=center>
<img src="./doc/llama3.png"/>
</div>
## 算法原理
Llama将输入embedding（将语句根据词汇量和词的位置、属性转换成数字化矩阵）后放入attention+ffn等提取特征，最后利用Softmax将解码器最后一层产生的未经归一化的分数向量（logits）转换为概率分布，其中每个元素表示生成对应词汇的概率，这使得模型可以生成一个分布，并从中选择最可能的词作为预测结果，然后一个字一个预测出来就是咱们看到的对话生成效果。
<div align=center>
<img src="./doc/algorithm.png"/>
</div>
## 环境配置
```
mv smollm_pytorch smollm # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.2-py3.10
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：83714c19d308
docker run -it --shm-size=64G -v $PWD/smollm:/home/smollm -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name smollm2 <your IMAGE ID> bash
```
### Dockerfile（方法二）
```
cd cd /home/smollm/docker
docker build --no-cache -t smollm2:latest .
docker run --shm-size=64G --name smollm2 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../smollm:/home/smollm -it smollm2 bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
cd /home/smollm
pip install -r finetuning/requirements.txt
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk24.04.2
python:python3.10
torch:2.3.0
torchvision:0.18.1
torchaudio:2.1.2
triton:2.1.0
flash-attn:2.0.4
deepspeed:0.14.2
apex:1.3.0
xformers:0.0.25
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
cd /home/smollm
pip install -r finetuning/requirements.txt
```
## 数据集
[bigcode/the-stack-smol](http://113.200.138.88:18080/aimodels/bigcode/the-stack-smol.git)
本项目已提供迷你数据集[`the-stack-smol/data/python`](./finetuning/bigcode/the-stack-smol/data/python/data.json)，可直接试用。
数据完整目录结构如下：
```
/home/smollm/finetuning/bigcode/the-stack-smol
├── README.md
├── data_generation.py
└── data
├── assembly
├── python
...
└── visual-basic
```
更多资料可参考源项目的[`README_origin`](./README_origin.md)
## 训练
```
关闭 wandb
wandb disabled
wandb offline
cd /home/smollm
sh train.sh
```
更多资料可参考源项目的[`README`](./finetuning/README.md)
## 推理
```
cd /home/smollm
# 方法一
python infer.py
# 方法二
trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu
```
更多资料可参考源项目的[`README`](./finetuning/README.md)
## result
推理方法一效果示例：
`输入: `
```
user:"Write a 100-word article on 'Benefits of Open-Source in AI research"
```
`输出:`
```
<|im_start|>system
You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
<|im_start|>user
Write a 100-word article on 'Benefits of Open-Source in AI research<|im_end|>
<|im_start|>assistant
Open-source in AI research offers numerous benefits. Firstly, it fosters collaboration and community engagement, accelerating the development of AI technologies. Secondly, it promotes transparency and accountability, as open-source projects are subject to peer review and scrutiny
```
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 应用场景
### 算法类别
`对话问答`
### 热点应用行业
`制造,广媒,金融,能源,医疗,家居,教育`
## 预训练权重
预训练权重快速下载中心：[SCNet AIModels](http://113.200.138.88:18080/aimodels) ，项目中的预训练权重可从快速下载通道下载：[SmolLM2-1.7B-Instruct](http://113.200.138.88:18080/aimodels/huggingfacetb/SmolLM2-1.7B-Instruct.git) 。
Hugging Face下载地址为：[HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
## 源码仓库及问题反馈
- http://developer.sourcefind.cn/codes/modelzoo/smollm_pytorch.git
## 参考资料
- https://github.com/huggingface/smollm.git
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# SmolLM2
SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.
You can find our most capable model **🤏 SmolLM2-1.7B-Instruct** [here](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct).
**New: Introducing [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk), the SFT dataset of SmolLM2 🚀**
<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/y45hIMNREW7w_XpHYB_0q.png" alt="Evaluation Results" width="600">
## Table of Contents
1. [Usage](#usage)
- [Transformers](#transformers)
- [Chat in TRL](#chat-in-trl)
- [Local inference](#local-inference)
2. [Smol-tools](#smol-tools)
3. [Pre-training](#pre-training)
4. [Fine-tuning](#fine-tuning)
5. [Evaluation](#evaluation)
6. [Synthetic data pipelines](#synthetic-data-pipelines)
## Usage
Our most powerful model is `SmolLM2-1.7B-Instruct`, which you can use as an assistant with `transformers`, `trl`, or using quantized versions with tools like `llama.cpp`, `MLX`, and `transformers.js`. For lighter applications, you can also use the smaller models `SmolLM2-360M` and`SmolLM2-135M`, which are suitable for on-device usage and can be integrated similarly.
All available in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9).
### Transformers
```bash
pip install transformers
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "Write a 100-word article on 'Benefits of Open-Source in AI research"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
### Chat in TRL
You can also use the TRL CLI to chat with the model from the terminal:
```bash
pip install trl
trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu
```
You can find more details on how to leverage the model for use cases such as text summarization, text rewriting and function calling in the model card: https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct
### Local inference
You can use the models locally with frameworks like `llama.cpp`, `MLX`, `MLC` and `transformers.js`. You can find the instructions to run SmolLM2 with these frameworks at [local-inference](local_inference/README.md).
## Smol-tools
A collection of lightweight AI-powered tools built with LLaMA.cpp and small language models. These tools are designed to run locally on your machine without requiring expensive GPU resources.
Further instructions on how to use the tools can be found in the [smol-tools README](smol_tools/README.md).
## Pre-training
You can find scripts for launching pre-training with [nanotron](https://github.com/huggingface/nanotron/) under [pre-training](pre-training/README.md), we share the exact configs for training SmolLM1 and will upload SmolLM2's configs soon.
## Fine-tuning
You can find an example script to finetune SmolLM2 using `TRL` and `PEFT` in the `finetuning` folder. We also link to our post-training scripts for SmolLM2 using the alignment handbook.
## Evaluation
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/T-cHJVA7FBaI0cgDApzEj.png)
You can find more detailed evaluation of each model size in the model cards in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9).
We use [lighteval](https://github.com/huggingface/lighteval) for all our evaluations, for more details refer to the [evaluation README](evaluation/README.md).
## Synthetic data pipelines
We released [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) the SFT dataset used for building SmolLM2 instruct models. It was created with [distilabel](https://github.com/argilla-io/distilabel) and you can check and execute the synthetic data pipelines in [distilabel_pipelines README](distilabel_pipelines/README.md)
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/JLTEbnsBQ_qY032mxFzgC.png" width="800"/>
<p><em>Comparison of models finetuned on SmolTalk and Orca AgentInstruct 1M. For more details, refer to the <a href="https://huggingface.co/datasets/HuggingFaceTB/smoltalk" target="_blank">dataset card</a>.</em></p>
</div>
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# distilabel pipelines
We used [`distilabel`](https://github.com/argilla-io/distilabel) to create several pipelines for generating instruction-following and multi-turn datasets for the post-training of SmolLM2.
> [!NOTE]
> This section is still in WIP. We will upload the rest of the pipelines soon. Thanks for your patience!
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# MagPie Ultra v1.0
This [`distilabel`](https://github.com/argilla-io/distilabel) was used to generate the [magpie-ultra-v1.0](https://huggingface.co/datasets/argilla/magpie-ultra-v1.0) dataset. The dataset follows the [MagPie](https://magpie-align.github.io) pipeline recipe to generate a multi-turn conversation dataset using [meta-llama/Llama-3.1-405B-Instruct-FP8](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct-FP8).
## Setup
You will need to install `distilabel` with a few extra dependencies to be able to execute the pipeline:
```bash
pip install distilabel[ray,vllm,sentence-transformers,faiss-cpu,hf-transformers]
```
\ No newline at end of file
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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.2-py3.10
ENV DEBIAN_FRONTEND=noninteractive
# RUN yum update && yum install -y git cmake wget build-essential
# RUN source /opt/dtk-24.04.2/env.sh
# # 安装pip相关依赖
COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
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transformers
trl
peft
accelerate
datasets
scipy
wandb # wandb offline & wandb disabled
bitsandbytes
\ No newline at end of file
docker_start.sh 0 → 100644
View file @ e75bc9be
docker run -it --shm-size=64G -v $PWD/smollm:/home/smollm -v /public/DL_DATA/AI:/home/AI -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name smollm 83714c19d308 bash
# python -m torch.utils.collect_env
evaluation/README.md 0 → 100644
View file @ e75bc9be
# SmolLM evaluation scripts
We're using the [LightEval](https://github.com/huggingface/lighteval/) library to benchmark our models.
Check out the [quick tour](https://github.com/huggingface/lighteval/wiki/Quicktour) to configure it to your own hardware and tasks.
## Setup
Use conda/venv with `python>=3.10`.
Adjust the pytorch installation according to your environment:
```bash
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu121
```
For reproducibility, we recommend fixed versions of the libraries:
```bash
pip install -r requirements.txt
```
## Running the evaluations
### SmolLM2 base models
```bash
lighteval accelerate \
--model_args "pretrained=HuggingFaceTB/SmolLM2-1.7B,revision=main,dtype=bfloat16,vllm,gpu_memory_utilisation=0.8,max_model_length=2048" \
--custom_tasks "tasks.py" --tasks "smollm2_base.txt" --output_dir "./evals" --save_details
```
### SmolLM2 instruction-tuned models
(note the `--use_chat_template` flag)
```bash
lighteval accelerate \
--model_args "pretrained=HuggingFaceTB/SmolLM2-1.7B-Instruct,revision=main,dtype=bfloat16,vllm,gpu_memory_utilisation=0.8,max_model_length=2048" \
--custom_tasks "tasks.py" --tasks "smollm2_instruct.txt" --use_chat_template --output_dir "./evals" --save_details
```
### MATH and other extra tasks
```bash
lighteval accelerate \
--model_args "pretrained=HuggingFaceTB/SmolLM2-1.7B-Instruct,revision=main,dtype=bfloat16,vllm,gpu_memory_utilisation=0.8,max_model_length=4096" \
--custom_tasks "tasks.py" --tasks "custom|math|4|1" --use_chat_template --output_dir "./evals" --save_details
```
evaluation/math_utils.py 0 → 100644
View file @ e75bc9be
import re
from typing import Optional
def math_normalizer(text: str) -> str:
def last_boxed_only_string(string: str) -> Optional[str]:
idx = string.rfind("\\boxed")
if "\\boxed " in string:
return "\\boxed " + string.split("\\boxed ")[-1].split("$")[0]
if idx < 0:
idx = string.rfind("\\fbox")
if idx < 0:
return None
i = idx
right_brace_idx = None
num_left_braces_open = 0
while i < len(string):
if string[i] == "{":
num_left_braces_open += 1
if string[i] == "}":
num_left_braces_open -= 1
if num_left_braces_open == 0:
right_brace_idx = i
break
i += 1
if right_brace_idx is None:
retval = None
else:
retval = string[idx: right_brace_idx + 1]
return retval
def remove_boxed(s: str) -> str:
if "\\boxed " in s:
left = "\\boxed "
#assert s[: len(left)] == left
return s[len(left):]
left = "\\boxed{"
#assert s[: len(left)] == left
#assert s[-1] == "}"
return s[len(left): -1]
SUBSTITUTIONS = [
("an ", ""),
("a ", ""),
(".$", "$"),
("\\$", ""),
(r"\ ", ""),
(" ", ""),
("mbox", "text"),
(",\\text{and}", ","),
("\\text{and}", ","),
("\\text{m}", "\\text{}"),
]
REMOVED_EXPRESSIONS = [
"square",
"ways",
"integers",
"dollars",
"mph",
"inches",
"ft",
"hours",
"km",
"units",
"\\ldots",
"sue",
"points",
"feet",
"minutes",
"digits",
"cents",
"degrees",
"cm",
"gm",
"pounds",
"meters",
"meals",
"edges",
"students",
"childrentickets",
"multiples",
"\\text{s}",
"\\text{.}",
"\\text{\ns}",
"\\text{}^2",
"\\text{}^3",
"\\text{\n}",
"\\text{}",
r"\mathrm{th}",
r"^\circ",
r"^{\circ}",
r"\;",
r",\!",
"{,}",
'"',
"\\dots",
]
def normalize_final_answer(final_answer: str) -> str:
"""
Normalize a final answer to a quantitative reasoning question.
Copied character for character from appendix D of Lewkowycz et al. (2022)
"""
final_answer = final_answer.split("=")[-1]
for before, after in SUBSTITUTIONS:
final_answer = final_answer.replace(before, after)
for expr in REMOVED_EXPRESSIONS:
final_answer = final_answer.replace(expr, "")
# Extract answer that is in LaTeX math, is bold,
# is surrounded by a box, etc.
final_answer = re.sub(r"(.*?)(\$)(.*?)(\$)(.*)", "$\\3$", final_answer)
final_answer = re.sub(r"(\\text\{)(.*?)(\})", "\\2", final_answer)
final_answer = re.sub(r"(\\textbf\{)(.*?)(\})", "\\2", final_answer)
final_answer = re.sub(r"(\\overline\{)(.*?)(\})", "\\2", final_answer)
final_answer = re.sub(r"(\\boxed\{)(.*)(\})", "\\2", final_answer)
# Normalize shorthand TeX:
# \fracab -> \frac{a}{b}
# \frac{abc}{bef} -> \frac{abc}{bef}
# \fracabc -> \frac{a}{b}c
# \sqrta -> \sqrt{a}
# \sqrtab -> sqrt{a}b
final_answer = re.sub(r"(frac)([^{])(.)", "frac{\\2}{\\3}", final_answer)
final_answer = re.sub(r"(sqrt)([^{])", "sqrt{\\2}", final_answer)
final_answer = final_answer.replace("$", "")
# Normalize 100,000 -> 100000
if final_answer.replace(",", "").isdigit():
final_answer = final_answer.replace(",", "")
return final_answer
if text is None:
return ""
boxed_string = last_boxed_only_string(text)
if boxed_string is None:
return ""
return normalize_final_answer(remove_boxed(boxed_string))
\ No newline at end of file
evaluation/requirements.txt 0 → 100644
View file @ e75bc9be
lighteval[accelerate,extended_tasks,vllm] @ git+https://github.com/huggingface/lighteval@ea46419a93fb390e8f694f7c6c64c1e684487c9d
fsspec>=2024.3.0
\ No newline at end of file
evaluation/smollm2_base.txt 0 → 100644
View file @ e75bc9be
custom|hellaswag|0|1
custom|arc|0|1
custom|piqa|0|1
custom|mmlu_pro|0|1
custom|commonsense_qa|0|1
custom|trivia_qa|0|1
custom|winogrande|0|1
custom|openbook_qa|0|1
custom|gsm8k|5|1
\ No newline at end of file
evaluation/smollm2_instruct.txt 0 → 100644
View file @ e75bc9be
extended|ifeval|0|0
custom|hellaswag|0|1
custom|arc|0|1
custom|piqa|0|1
custom|mmlu_pro|0|1
custom|bbh|3|1
custom|gsm8k|5|1
\ No newline at end of file
evaluation/tasks.py 0 → 100644
View file @ e75bc9be
import re
import numpy as np
from lighteval.tasks.lighteval_task import LightevalTaskConfig
from lighteval.tasks.requests import Doc
from lighteval.metrics.metrics import Metrics, SampleLevelMetric, MetricCategory, MetricUseCase, ExactMatches
import lighteval.tasks.default_prompts as prompt
from .math_utils import math_normalizer
def prompt_hellaswag(line, task_name: str = None):
def preprocess(text):
"""Comes from AiHarness"""
# text = text.strip()
# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
text = text.replace(" [title]", ". ")
text = re.sub("\\[.*?\\]", "", text)
text = text.replace(" ", " ")
return text
ctx = f"{line['ctx_a']} {line['ctx_b'].capitalize()} "
return Doc(
task_name=task_name,
query=preprocess(line["activity_label"] + ": " + ctx),
choices=[" " + preprocess(ending) for ending in line["endings"]],
gold_index=int(line["label"]) if line["label"] != "" else -1, # -1 for test
)
def prompt_commonsense_qa(line, task_name: str = None):
return Doc(
task_name=task_name,
query=line["question"],
choices=[f" {c}" for c in line["choices"]["text"]],
gold_index=line["choices"]["label"].index(line["answerKey"].strip()),
instruction="",
)
def mmlu_pro_mc_prompt(line, task_name: str = None):
options = line["options"]
letters = [chr(ord("A") + i) for i in range(len(options))]
topic = line["category"].replace('_', ' ')
query = f"The following are multiple choice questions (with answers) about {topic}.\n\n"
query += line["question"] + "\n"
query += "".join([f"{letter}. {choice}\n" for letter, choice in zip(letters, options)])
query += "Answer:"
return Doc(
task_name=task_name,
query=query,
choices=letters,
gold_index=line["answer_index"],
instruction=f"The following are multiple choice questions (with answers) about {topic}.\n\n",
)
def bbh_prompt(line, task_name: str = None):
return Doc(
task_name=task_name,
query="Question: " + line["input"] + "\nAnswer: ",
choices=[line["target"]],
gold_index=0,
)
def prompt_math(line, task_name: str = None):
return Doc(
task_name=task_name,
query=f"Problem:\n{line['problem']}\n\nSolution:\n",
gold_index=0,
choices=[f"{line['solution']}\n"],
)
TASKS_TABLE = [
LightevalTaskConfig(
name="arc:easy",
prompt_function=prompt.arc,
suite=["custom"],
hf_repo="ai2_arc",
hf_revision="210d026faf9955653af8916fad021475a3f00453",
hf_subset=f"ARC-Easy",
evaluation_splits=["test"],
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="arc:challenge",
prompt_function=prompt.arc,
suite=["custom"],
hf_repo="ai2_arc",
hf_revision="210d026faf9955653af8916fad021475a3f00453",
hf_subset=f"ARC-Challenge",
evaluation_splits=["test"],
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="openbook_qa",
prompt_function=prompt.openbookqa,
suite=["custom"],
hf_repo="allenai/openbookqa",
hf_subset="main",
hf_revision="388097ea7776314e93a529163e0fea805b8a6454",
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="hellaswag",
prompt_function=prompt_hellaswag,
suite=["custom"],
hf_repo="Rowan/hellaswag",
hf_subset="default",
hf_revision="6002345709e0801764318f06bf06ce1e7d1a1fe3",
trust_dataset=True,
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="commonsense_qa",
prompt_function=prompt_commonsense_qa,
suite=["custom"],
hf_repo="tau/commonsense_qa",
hf_subset="default",
hf_revision="94630fe30dad47192a8546eb75f094926d47e155",
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="winogrande",
prompt_function=prompt.winogrande,
suite=["custom"],
hf_repo="allenai/winogrande",
hf_subset="winogrande_xl",
hf_revision="85ac5b5a3b7a930e22d590176e39460400d19e41",
trust_dataset=True,
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="piqa",
prompt_function=prompt.piqa_harness,
suite=["custom"],
hf_repo="ybisk/piqa",
hf_subset="plain_text",
hf_revision="2e8ac2dffd59bac8c3c6714948f4c551a0848bb0",
trust_dataset=True,
metric=[Metrics.loglikelihood_acc_norm_nospace],
),
LightevalTaskConfig(
name="trivia_qa",
prompt_function=prompt.triviaqa,
suite=["custom"],
hf_repo="mandarjoshi/trivia_qa",
hf_subset="rc.nocontext",
hf_revision="0f7faf33a3908546c6fd5b73a660e0f8ff173c2f",
hf_avail_splits=["train", "validation"],
evaluation_splits=["validation"],
metric=[Metrics.quasi_exact_match_triviaqa],
generation_size=20,
trust_dataset=True,
stop_sequence=["Question:", "Question"],
few_shots_select="random_sampling_from_train",
),
LightevalTaskConfig(
name="mmlu_pro",
prompt_function=mmlu_pro_mc_prompt,
suite=["custom"],
hf_repo="TIGER-Lab/MMLU-Pro",
hf_subset="default",
hf_revision="3373e0b32277875b8db2aa555a333b78a08477ea",
metric=[Metrics.loglikelihood_acc_norm_nospace],
evaluation_splits=["test"],
few_shots_split="validation",
),
LightevalTaskConfig(
name="gsm8k",
prompt_function=prompt.gsm8k,
suite=["custom"],
hf_repo="openai/gsm8k",
hf_subset="main",
hf_revision="e53f048856ff4f594e959d75785d2c2d37b678ee",
hf_avail_splits=["train", "test"],
evaluation_splits=["test"],
metric=[Metrics.quasi_exact_match_gsm8k],
generation_size=256,
stop_sequence=["Question:", "Question"],
few_shots_select="random_sampling_from_train",
)
]
BBH_TASKS = [
LightevalTaskConfig(
name=f"bbh:{subset}",
prompt_function=bbh_prompt,
suite=["custom"],
hf_repo="lighteval/big_bench_hard",
hf_subset=subset,
hf_revision="80610173426f05e6f1448f047e2db4840a7dd899",
metric=[Metrics.exact_match],
hf_avail_splits=["train"],
# this is the only split available, obviously not used in training
evaluation_splits=["train"],
few_shots_split="train",
trust_dataset=True,
stop_sequence=["Question:", "Question"],
)
for subset in [
"boolean_expressions",
"causal_judgement",
"date_understanding",
"disambiguation_qa",
"dyck_languages",
"formal_fallacies",
"geometric_shapes",
"hyperbaton",
"logical_deduction_five_objects",
"logical_deduction_seven_objects",
"logical_deduction_three_objects",
"movie_recommendation",
"multistep_arithmetic_two",
"navigate",
"object_counting",
"penguins_in_a_table",
"reasoning_about_colored_objects",
"ruin_names",
"salient_translation_error_detection",
"snarks",
"sports_understanding",
"temporal_sequences",
"tracking_shuffled_objects_five_objects",
"tracking_shuffled_objects_seven_objects",
"tracking_shuffled_objects_three_objects",
"web_of_lies",
"word_sorting",
]
]
TASKS_TABLE.extend(BBH_TASKS)
quasi_exact_match_math = SampleLevelMetric(
metric_name="qem",
sample_level_fn=ExactMatches(
strip_strings=True, normalize_pred=math_normalizer, normalize_gold=math_normalizer
).compute,
category=MetricCategory.GENERATIVE,
use_case=MetricUseCase.MATH,
corpus_level_fn=np.mean,
higher_is_better=True,
)
MATH_TASKS = [
LightevalTaskConfig(
name=f"math:{subset}",
prompt_function=prompt_math,
suite=["custom"],
hf_repo="HuggingFaceTB/MATH",
hf_subset=subset,
hf_revision="140a673f1f7182daf7923fdc7108e8cdbf97df46",
hf_avail_splits=["train", "test"],
evaluation_splits=["test"],
metric=[quasi_exact_match_math],
generation_size=1024,
stop_sequence=["Problem:"],
few_shots_split="fewshot",
few_shots_select="sequential",
trust_dataset=True,
)
for subset in [
"algebra",
"counting_and_probability",
"geometry",
"intermediate_algebra",
"number_theory",
"prealgebra",
"precalculus",
]
]
TASKS_TABLE.extend(MATH_TASKS)
if __name__ == "__main__":
print(t.name for t in TASKS_TABLE)
print(len(TASKS_TABLE))
\ No newline at end of file
finetuning/Dockerfile 0 → 100644
View file @ e75bc9be
# base image: CUDA 12.1
FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
WORKDIR /app
# install necessary packages
RUN apt-get update && apt-get install -y \
git \
wget \
curl \
ca-certificates \
libglib2.0-0 \
libsm6 \
libxrender1 \
libxext6 \
libssl-dev \
libffi-dev \
python3 \
python3-pip \
&& rm -rf /var/lib/apt/lists/*
# set python3 as default python
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1
RUN pip install --upgrade pip setuptools
RUN pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu121
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
ENV PYTHONUNBUFFERED=1
CMD ["bash"]
\ No newline at end of file
finetuning/HuggingFaceTB/SmolLM2-1.7B-Instruct/README.md 0 → 100644
View file @ e75bc9be
---
library_name: transformers
license: apache-2.0
language:
- en
---
# SmolLM2
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/y45hIMNREW7w_XpHYB_0q.png)
## Table of Contents
1. [Model Summary](#model-summary)
2. [Evaluation](#evaluation)
3. [Examples](#examples)
4. [Limitations](#limitations)
5. [Training](#training)
6. [License](#license)
7. [Citation](#citation)
## Model Summary
SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.
The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1).
### How to use
### Transformers
```bash
pip install transformers
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "What is the capital of France."}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
### Chat in TRL
You can also use the TRL CLI to chat with the model from the terminal:
```bash
pip install trl
trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu
```
## Evaluation
In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.
## Base Pre-Trained Model
| Metric | SmolLM2-1.7B | Llama-1B | Qwen2.5-1.5B | SmolLM1-1.7B |
|------------------|--------------|-------------|---------------|--------------|
| HellaSwag | **68.7** | 61.2 | 66.4 | 62.9 |
| ARC (Average) | **60.5** | 49.2 | 58.5 | 59.9 |
| PIQA | **77.6** | 74.8 | 76.1 | 76.0 |
| MMLU-Pro (MCF) | **19.4** | 11.7 | 13.7 | 10.8 |
| CommonsenseQA | **43.6** | 41.2 | 34.1 | 38.0 |
| TriviaQA | **36.7** | 28.1 | 20.9 | 22.5 |
| Winogrande | **59.4** | 57.8 | 59.3 | 54.7 |
| OpenBookQA | 42.2 | 38.4 | 40.0 | **42.4** |
| GSM8K (5-shot) | 31.0 | 7.2 | **61.3** | 5.5 |
## Instruction Model
| Metric | SmolLM2-1.7B-Instruct | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct | SmolLM1-1.7B-Instruct |
|:-----------------------------|:---------------------:|:-----------------:|:----------------------:|:----------------------:|
| IFEval (Average prompt/inst) | **56.7** | 53.5 | 47.4 | 23.1 |
| MT-Bench | 6.13 | 5.48 | **6.52** | 4.33 |
| OpenRewrite-Eval (micro_avg RougeL) | 44.9 | 39.2 | **46.9** | NaN |
| HellaSwag | **66.1** | 56.1 | 60.9 | 55.5 |
| ARC (Average) | **51.7** | 41.6 | 46.2 | 43.7 |
| PIQA | **74.4** | 72.3 | 73.2 | 71.6 |
| MMLU-Pro (MCF) | 19.3 | 12.7 | **24.2** | 11.7 |
| BBH (3-shot) | 32.2 | 27.6 | **35.3** | 25.7 |
| GSM8K (5-shot) | **48.2** | 26.8 | 42.8 | 4.62 |
## Examples
Below are some system and instruct prompts that work well for special tasks
### Text rewriting
```python
system_prompt_rewrite = "You are an AI writing assistant. Your task is to rewrite the user's email to make it more professional and approachable while maintaining its main points and key message. Do not return any text other than the rewritten message."
user_prompt_rewrite = "Rewrite the message below to make it more friendly and approachable while maintaining its main points and key message. Do not add any new information or return any text other than the rewritten message\nThe message:"
messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content":f"{user_prompt_rewrite} The CI is failing after your last commit!"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
```
Hey there! I noticed that the CI isn't passing after your latest commit. Could you take a look and let me know what's going on? Thanks so much for your help!
```
### Summarization
```python
system_prompt_summarize = "Provide a concise, objective summary of the input text in up to three sentences, focusing on key actions and intentions without using second or third person pronouns."
messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content": INSERT_LONG_EMAIL]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
### Function calling
SmolLM2-1.7B-Instruct can handle function calling, it scores 27% on the [BFCL Leaderboard](https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html). Here's how you can leverage it:
```python
import json
import re
from typing import Optional
from jinja2 import Template
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import get_json_schema
system_prompt = Template("""You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the functions can be used, point it out and refuse to answer.
If the given question lacks the parameters required by the function, also point it out.
You have access to the following tools:
<tools>{{ tools }}</tools>
The output MUST strictly adhere to the following format, and NO other text MUST be included.
The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list '[]'.
<tool_call>[
{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
... (more tool calls as required)
]</tool_call>""")
def prepare_messages(
query: str,
tools: Optional[dict[str, any]] = None,
history: Optional[list[dict[str, str]]] = None
) -> list[dict[str, str]]:
"""Prepare the system and user messages for the given query and tools.
Args:
query: The query to be answered.
tools: The tools available to the user. Defaults to None, in which case if a
list without content will be passed to the model.
history: Exchange of messages, including the system_prompt from
the first query. Defaults to None, the first message in a conversation.
"""
if tools is None:
tools = []
if history:
messages = history.copy()
messages.append({"role": "user", "content": query})
else:
messages = [
{"role": "system", "content": system_prompt.render(tools=json.dumps(tools))},
{"role": "user", "content": query}
]
return messages
def parse_response(text: str) -> str | dict[str, any]:
"""Parses a response from the model, returning either the
parsed list with the tool calls parsed, or the
model thought or response if couldn't generate one.
Args:
text: Response from the model.
"""
pattern = r"<tool_call>(.*?)</tool_call>"
matches = re.findall(pattern, text, re.DOTALL)
if matches:
return json.loads(matches[0])
return text
model_name_smollm = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name_smollm, device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_smollm)
from datetime import datetime
import random
def get_current_time() -> str:
"""Returns the current time in 24-hour format.
Returns:
str: Current time in HH:MM:SS format.
"""
return datetime.now().strftime("%H:%M:%S")
def get_random_number_between(min: int, max: int) -> int:
"""
Gets a random number between min and max.
Args:
min: The minimum number.
max: The maximum number.
Returns:
A random number between min and max.
"""
return random.randint(min, max)
tools = [get_json_schema(get_random_number_between), get_json_schema(get_current_time)]
toolbox = {"get_random_number_between": get_random_number_between, "get_current_time": get_current_time}
query = "Give me a number between 1 and 300"
messages = prepare_messages(query, tools=tools)
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
tool_calls = parse_response(result)
# [{'name': 'get_random_number_between', 'arguments': {'min': 1, 'max': 300}}
# Get tool responses
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# [63]
# For the second turn, rebuild the history of messages:
history = messages.copy()
# Add the "parsed response"
history.append({"role": "assistant", "content": result})
query = "Can you give me the hour?"
history.append({"role": "user", "content": query})
inputs = tokenizer.apply_chat_template(history, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
tool_calls = parse_response(result)
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# ['07:57:25']
```
More details such as parallel function calls and tools not available can be found [here](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/blob/main/instructions_function_calling.md)
## Limitations
SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
## Training
### Model
- **Architecture:** Transformer decoder
- **Pretraining tokens:** 11T
- **Precision:** bfloat16
### Hardware
- **GPUs:** 256 H100
### Software
- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main)
- **Alignement Handbook** [alignement-handbook](https://github.com/huggingface/alignment-handbook/)
## License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Citation
```bash
@misc{allal2024SmolLM2,
title={SmolLM2 - with great data, comes great performance},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf},
year={2024},
}
```
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