falcon rw 1b code generation llm task2

ProviderKatochh
Categorycode-generation
Licenseapache-2.0
Downloads3
Stars0

Overview

Falcon RW 1B is a compact, code-centric language model designed for efficient deployment in resource-constrained environments. Unlike massive general-purpose LLMs, this 1-billion parameter model focuses on high-density code generation and completion, making it an ideal candidate for local IDE integration, edge computing, or as a specialized component in a larger RAG pipeline. It balances a small memory footprint with the ability to handle common programming syntax and logic tasks. For developers, this means faster inference speeds and lower latency when implementing real-time autocomplete or basic boilerplate generation without needing heavy GPU clusters.

Highlights

  • Compact 1B parameter size for low-latency local deployment
  • Optimized specifically for code generation and completion tasks
  • Apache-2.0 license ensures flexible commercial integration
  • Ideal for IDE plugins and resource-constrained edge environments

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Katochh/falcon-rw-1b-code-generation-llm-task2")
tokenizer = AutoTokenizer.from_pretrained("Katochh/falcon-rw-1b-code-generation-llm-task2")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download Katochh/falcon-rw-1b-code-generation-llm-task2

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download Katochh/falcon-rw-1b-code-generation-llm-task2 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Katochh/falcon-rw-1b-code-generation-llm-task2')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/Katochh/falcon-rw-1b-code-generation-llm-task2

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Katochh/falcon-rw-1b-code-generation-llm-task2

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Katochh/falcon-rw-1b-code-generation-llm-task2')
tokenizer = AutoTokenizer.from_pretrained('Katochh/falcon-rw-1b-code-generation-llm-task2')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
library_name: peft
tags:

  • trl

  • sft

  • generated_from_trainer

base_model: petals-team/falcon-rw-1b
model-index:
  • name: falcon-rw-1b-code-generation-llm-task2

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-rw-1b-code-generation-llm-task2

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: 1.0581

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: 0.0001

  • 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3318 | 0.1 | 20 | 1.3407 |
| 1.2643 | 0.2 | 40 | 1.1844 |
| 1.1681 | 0.3 | 60 | 1.1522 |
| 1.0891 | 0.4 | 80 | 1.1209 |
| 1.2164 | 0.5 | 100 | 1.1265 |
| 1.0855 | 0.6 | 120 | 1.1010 |
| 1.1129 | 0.7 | 140 | 1.0897 |
| 1.1169 | 0.8 | 160 | 1.0799 |
| 1.0664 | 0.9 | 180 | 1.0706 |
| 1.1483 | 1.0 | 200 | 1.0756 |
| 0.9707 | 1.1 | 220 | 1.0625 |
| 1.0102 | 1.2 | 240 | 1.0624 |
| 1.0805 | 1.3 | 260 | 1.0615 |
| 0.969 | 1.4 | 280 | 1.0580 |
| 1.118 | 1.5 | 300 | 1.0582 |
| 0.9883 | 1.6 | 320 | 1.0581 |

Framework versions

  • PEFT 0.10.0
  • Transformers 4.40.0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1
View official source
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