falcon code generation llm
简介
核心亮点
- 支持多语言代码生成,高效补全开发逻辑
- Apache-2.0 协议,企业级部署无版权压力
- 低门槛快速上手,适配多种 IDE 集成场景
- 适合构建私有化代码助手,保障代码安全性
使用方法
# 安装 Hugging Face transformers
pip install transformers torch
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("Katochh/falcon-code-generation-llm")
tokenizer = AutoTokenizer.from_pretrained("Katochh/falcon-code-generation-llm")
Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引:在下载前,请先通过如下命令安装 huggingface_hub:
pip install -U huggingface_hub
命令行下载
下载完整模型库
huggingface-cli download Katochh/falcon-code-generation-llm
下载单个文件到指定本地文件夹(以下载 config.json 到当前路径下 ./dir 目录为例)
huggingface-cli download Katochh/falcon-code-generation-llm config.json --local-dir ./dir
SDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Katochh/falcon-code-generation-llm')
Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/Katochh/falcon-code-generation-llm
如果您希望跳过 lfs 大文件下载,可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Katochh/falcon-code-generation-llm
模型文件托管在 Hugging Face Hub,使用 HF CLI / SDK / Git 直接下载,不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch
模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('Katochh/falcon-code-generation-llm')
tokenizer = AutoTokenizer.from_pretrained('Katochh/falcon-code-generation-llm')
完整文档
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: petals-team/falcon-rw-1b
model-index:
- name: falcon-code-generation-llm
results: []
---
<!-- 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. -->
falcon-code-generation-llm
This model is a fine-tuned version of petals-team/falcon-rw-1b on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8328
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 320
Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1898 | 0.1 | 20 | 1.9930 |
| 2.0474 | 0.2 | 40 | 1.9372 |
| 1.8768 | 0.3 | 60 | 1.9180 |
| 2.0356 | 0.4 | 80 | 1.8915 |
| 1.946 | 0.5 | 100 | 1.9185 |
| 1.9219 | 0.6 | 120 | 1.8740 |
| 1.973 | 0.7 | 140 | 1.8762 |
| 1.8046 | 0.8 | 160 | 1.8549 |
| 1.8934 | 0.9 | 180 | 1.8451 |
| 1.8365 | 1.0 | 200 | 1.8525 |
| 1.7949 | 1.1 | 220 | 1.8343 |
| 1.703 | 1.2 | 240 | 1.8443 |
| 1.6269 | 1.3 | 260 | 1.8453 |
| 1.6731 | 1.4 | 280 | 1.8330 |
| 1.6004 | 1.5 | 300 | 1.8331 |
| 1.7031 | 1.6 | 320 | 1.8328 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1