text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
|---|---|---|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/tests/models/sew_d/test_modeling_sew_d.py/0 | {
"file_path": "transformers/tests/models/sew_d/test_modeling_sew_d.py",
"repo_id": "transformers",
"token_count": 10675
} |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/tests/models/speecht5/test_modeling_speecht5.py/0 | {
"file_path": "transformers/tests/models/speecht5/test_modeling_speecht5.py",
"repo_id": "transformers",
"token_count": 36221
} |
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | transformers/tests/models/videomae/test_image_processing_videomae.py/0 | {
"file_path": "transformers/tests/models/videomae/test_image_processing_videomae.py",
"repo_id": "transformers",
"token_count": 3761
} |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | transformers/tests/models/vitmatte/test_image_processing_vitmatte.py/0 | {
"file_path": "transformers/tests/models/vitmatte/test_image_processing_vitmatte.py",
"repo_id": "transformers",
"token_count": 3654
} |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py/0 | {
"file_path": "transformers/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py",
"repo_id": "transformers",
"token_count": 17509
} |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | transformers/tests/models/xlm/test_tokenization_xlm.py/0 | {
"file_path": "transformers/tests/models/xlm/test_tokenization_xlm.py",
"repo_id": "transformers",
"token_count": 1536
} |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/tests/models/yolos/test_modeling_yolos.py/0 | {
"file_path": "transformers/tests/models/yolos/test_modeling_yolos.py",
"repo_id": "transformers",
"token_count": 6807
} |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/tests/pipelines/test_pipelines_table_question_answering.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_table_question_answering.py",
"repo_id": "transformers",
"token_count": 14916
} |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | transformers/tests/quantization/autoawq/test_awq.py/0 | {
"file_path": "transformers/tests/quantization/autoawq/test_awq.py",
"repo_id": "transformers",
"token_count": 8896
} |
# coding=utf-8
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | transformers/tests/quantization/ggml/test_ggml.py/0 | {
"file_path": "transformers/tests/quantization/ggml/test_ggml.py",
"repo_id": "transformers",
"token_count": 17906
} |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/tests/repo_utils/test_get_test_info.py/0 | {
"file_path": "transformers/tests/repo_utils/test_get_test_info.py",
"repo_id": "transformers",
"token_count": 2131
} |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | transformers/tests/test_feature_extraction_common.py/0 | {
"file_path": "transformers/tests/test_feature_extraction_common.py",
"repo_id": "transformers",
"token_count": 828
} |
# coding=utf-8
# Copyright 2018 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/tests/trainer/test_trainer.py/0 | {
"file_path": "transformers/tests/trainer/test_trainer.py",
"repo_id": "transformers",
"token_count": 110809
} |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/tests/utils/test_backbone_utils.py/0 | {
"file_path": "transformers/tests/utils/test_backbone_utils.py",
"repo_id": "transformers",
"token_count": 5009
} |
import os
import unittest
from pathlib import Path
from transformers.utils.import_utils import define_import_structure, spread_import_structure
import_structures = Path("import_structures")
def fetch__all__(file_content):
"""
Returns the content of the __all__ variable in the file content.
Returns None... | transformers/tests/utils/test_import_structure.py/0 | {
"file_path": "transformers/tests/utils/test_import_structure.py",
"repo_id": "transformers",
"token_count": 2227
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | transformers/utils/check_bad_commit.py/0 | {
"file_path": "transformers/utils/check_bad_commit.py",
"repo_id": "transformers",
"token_count": 2747
} |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/utils/check_tf_ops.py/0 | {
"file_path": "transformers/utils/check_tf_ops.py",
"repo_id": "transformers",
"token_count": 1302
} |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/utils/notification_service.py/0 | {
"file_path": "transformers/utils/notification_service.py",
"repo_id": "transformers",
"token_count": 25355
} |
#!/bin/bash
# This script runs an SFT example end-to-end on a tiny model using different possible configurations
# but defaults to QLoRA + PEFT
OUTPUT_DIR="test_sft/"
MODEL_NAME="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
DATASET_NAME="stanfordnlp/imdb"
MAX_STEPS=5
BATCH_SIZE=2
SEQ_LEN=128
# Handle extra argumen... | trl/commands/run_sft.sh/0 | {
"file_path": "trl/commands/run_sft.sh",
"repo_id": "trl",
"token_count": 626
} |
# Detoxifying a Language Model using PPO
Language models (LMs) are known to sometimes generate toxic outputs. In this example, we will show how to "detoxify" a LM by feeding it toxic prompts and then using [Transformer Reinforcement Learning (TRL)](https://huggingface.co/docs/trl/index) and Proximal Policy Optimizatio... | trl/docs/source/detoxifying_a_lm.md/0 | {
"file_path": "trl/docs/source/detoxifying_a_lm.md",
"repo_id": "trl",
"token_count": 3789
} |
# Use model after training
Once you have trained a model using either the SFTTrainer, PPOTrainer, or DPOTrainer, you will have a fine-tuned model that can be used for text generation. In this section, we'll walk through the process of loading the fine-tuned model and generating text. If you need to run an inference se... | trl/docs/source/use_model.md/0 | {
"file_path": "trl/docs/source/use_model.md",
"repo_id": "trl",
"token_count": 778
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/examples/datasets/rlaif-v.py/0 | {
"file_path": "trl/examples/datasets/rlaif-v.py",
"repo_id": "trl",
"token_count": 1594
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py/0 | {
"file_path": "trl/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py",
"repo_id": "trl",
"token_count": 3977
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/examples/scripts/dpo_vlm.py/0 | {
"file_path": "trl/examples/scripts/dpo_vlm.py",
"repo_id": "trl",
"token_count": 1983
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/examples/scripts/xpo.py/0 | {
"file_path": "trl/examples/scripts/xpo.py",
"repo_id": "trl",
"token_count": 1865
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/tests/test_bco_trainer.py/0 | {
"file_path": "trl/tests/test_bco_trainer.py",
"repo_id": "trl",
"token_count": 8319
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/tests/test_iterative_sft_trainer.py/0 | {
"file_path": "trl/tests/test_iterative_sft_trainer.py",
"repo_id": "trl",
"token_count": 2179
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/tests/test_utils.py/0 | {
"file_path": "trl/tests/test_utils.py",
"repo_id": "trl",
"token_count": 9869
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/models/auxiliary_modules.py/0 | {
"file_path": "trl/trl/models/auxiliary_modules.py",
"repo_id": "trl",
"token_count": 1377
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/trainer/alignprop_config.py/0 | {
"file_path": "trl/trl/trainer/alignprop_config.py",
"repo_id": "trl",
"token_count": 3895
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/trainer/judges.py/0 | {
"file_path": "trl/trl/trainer/judges.py",
"repo_id": "trl",
"token_count": 7488
} |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/trainer/rloo_config.py/0 | {
"file_path": "trl/trl/trainer/rloo_config.py",
"repo_id": "trl",
"token_count": 1717
} |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | accelerate/benchmarks/fp8/ms_amp/ddp.py/0 | {
"file_path": "accelerate/benchmarks/fp8/ms_amp/ddp.py",
"repo_id": "accelerate",
"token_count": 1938
} |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | accelerate/docs/source/concept_guides/performance.md/0 | {
"file_path": "accelerate/docs/source/concept_guides/performance.md",
"repo_id": "accelerate",
"token_count": 1476
} |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | accelerate/docs/source/usage_guides/local_sgd.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/local_sgd.md",
"repo_id": "accelerate",
"token_count": 1475
} |
# This config template simply setups up the TransformersEngine config (and a config for a single GPU),
# this can interop with the other configs in this folder
distributed_type: "NO"
mixed_precision: "fp8"
# Then we specify the fp8 configuration:
fp8_config:
backend: TE # Can be TE | MS-AMP
# The following are TE s... | accelerate/examples/config_yaml_templates/fp8.yaml/0 | {
"file_path": "accelerate/examples/config_yaml_templates/fp8.yaml",
"repo_id": "accelerate",
"token_count": 239
} |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | accelerate/examples/inference/distributed/phi2.py/0 | {
"file_path": "accelerate/examples/inference/distributed/phi2.py",
"repo_id": "accelerate",
"token_count": 1161
} |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/manim_animations/big_model_inference/stage_1.py/0 | {
"file_path": "accelerate/manim_animations/big_model_inference/stage_1.py",
"repo_id": "accelerate",
"token_count": 1904
} |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/accelerator.py/0 | {
"file_path": "accelerate/src/accelerate/accelerator.py",
"repo_id": "accelerate",
"token_count": 72627
} |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/test_utils/scripts/test_ddp_comm_hook.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/test_ddp_comm_hook.py",
"repo_id": "accelerate",
"token_count": 1232
} |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/utils/fsdp_utils.py/0 | {
"file_path": "accelerate/src/accelerate/utils/fsdp_utils.py",
"repo_id": "accelerate",
"token_count": 7632
} |
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"zero_optimization": {
"stage": 2,
"offload_optimizer":... | accelerate/tests/deepspeed/ds_config_zero2_model_only.json/0 | {
"file_path": "accelerate/tests/deepspeed/ds_config_zero2_model_only.json",
"repo_id": "accelerate",
"token_count": 427
} |
compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: 'NO'
downcast_bf16: 'no'
fsdp_config: {}
gpu_ids: all
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
megatron_lm_config: {}
mixed_precision: 'no'
num_machines: 1
num_processes: 1
rdzv_backend: static
sa... | accelerate/tests/test_configs/latest.yaml/0 | {
"file_path": "accelerate/tests/test_configs/latest.yaml",
"repo_id": "accelerate",
"token_count": 186
} |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/tests/test_sagemaker.py/0 | {
"file_path": "accelerate/tests/test_sagemaker.py",
"repo_id": "accelerate",
"token_count": 1007
} |
# Using the hub
Install the [`hf-hub`](https://github.com/huggingface/hf-hub) crate:
```bash
cargo add hf-hub
```
Then let's start by downloading the [model file](https://huggingface.co/bert-base-uncased/tree/main).
```rust
# extern crate candle_core;
# extern crate hf_hub;
use hf_hub::api::sync::Api;
use candle_c... | candle/candle-book/src/inference/hub.md/0 | {
"file_path": "candle/candle-book/src/inference/hub.md",
"repo_id": "candle",
"token_count": 1098
} |
pub(crate) mod affine;
pub(crate) mod conv_transpose2d;
pub(crate) mod matmul;
pub(crate) mod qmatmul;
pub(crate) mod random;
pub(crate) mod reduce;
pub(crate) mod unary;
pub(crate) mod where_cond;
use candle_core::{Device, Result};
pub(crate) trait BenchDevice {
fn sync(&self) -> Result<()>;
fn bench_name<S... | candle/candle-core/benches/benchmarks/mod.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/mod.rs",
"repo_id": "candle",
"token_count": 1064
} |
#![allow(clippy::excessive_precision)]
// Code taken from https://github.com/statrs-dev/statrs
//! Provides the [error](https://en.wikipedia.org/wiki/Error_function) and
//! related functions
mod evaluate {
//! Provides functions that don't have a numerical solution and must
//! be solved computationally (e.g.... | candle/candle-core/src/cpu/erf.rs/0 | {
"file_path": "candle/candle-core/src/cpu/erf.rs",
"repo_id": "candle",
"token_count": 11974
} |
//! Implementation of the Cuda backend when Cuda support has not been compiled in.
//!
#![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct CudaDevice;
#[derive(Debug)]
pub struct CudaStorage;
macr... | candle/candle-core/src/dummy_cuda_backend.rs/0 | {
"file_path": "candle/candle-core/src/dummy_cuda_backend.rs",
"repo_id": "candle",
"token_count": 3405
} |
//! Support for the GGML file format.
use super::{k_quants, GgmlDType, QStorage};
use crate::{Device, Result};
use byteorder::{LittleEndian, ReadBytesExt};
use std::collections::HashMap;
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.h#L37
#[derive(Debug, Clone, Copy, Pa... | candle/candle-core/src/quantized/ggml_file.rs/0 | {
"file_path": "candle/candle-core/src/quantized/ggml_file.rs",
"repo_id": "candle",
"token_count": 4584
} |
use crate::{shape::Dim, Context, Error, Result, Shape, Tensor};
impl Tensor {
/// Concatenates two or more tensors along a particular dimension.
///
/// All tensors must of the same rank, and the output will have
/// the same rank
///
/// ```rust
/// # use candle_core::{Tensor, DType, Devic... | candle/candle-core/src/tensor_cat.rs/0 | {
"file_path": "candle/candle-core/src/tensor_cat.rs",
"repo_id": "candle",
"token_count": 6380
} |
use candle_core::{
bail,
quantized::{self, GgmlDType},
test_device,
test_utils::to_vec2_round,
DType, Device, IndexOp, Module, Result, Tensor,
};
use quantized::{k_quants, GgmlType};
use rand::prelude::*;
const GGML_TEST_SIZE: usize = 32 * 128;
const GGML_MAX_QUANTIZATION_TOTAL_ERROR: f32 = 0.002;... | candle/candle-core/tests/quantized_tests.rs/0 | {
"file_path": "candle/candle-core/tests/quantized_tests.rs",
"repo_id": "candle",
"token_count": 21659
} |
use candle::Tensor;
pub struct Dataset {
pub train_images: Tensor,
pub train_labels: Tensor,
pub test_images: Tensor,
pub test_labels: Tensor,
pub labels: usize,
}
pub mod cifar;
pub mod mnist;
| candle/candle-datasets/src/vision/mod.rs/0 | {
"file_path": "candle/candle-datasets/src/vision/mod.rs",
"repo_id": "candle",
"token_count": 92
} |
# candle-clip
Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
pairs of images with related texts.
https://github.com/openai/CLIP
https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip
## Running on an example on cpu
```
$ ... | candle/candle-examples/examples/clip/README.md/0 | {
"file_path": "candle/candle-examples/examples/clip/README.md",
"repo_id": "candle",
"token_count": 623
} |
use enterpolation::linear::ConstEquidistantLinear;
use enterpolation::Generator;
use palette::LinSrgb;
use candle::Tensor;
pub struct SpectralRColormap {
gradient: ConstEquidistantLinear<f32, LinSrgb, 9>,
}
impl SpectralRColormap {
pub(crate) fn new() -> Self {
// Define a colormap similar to 'Spectr... | candle/candle-examples/examples/depth_anything_v2/color_map.rs/0 | {
"file_path": "candle/candle-examples/examples/depth_anything_v2/color_map.rs",
"repo_id": "candle",
"token_count": 896
} |
//! EVA-02: Explore the limits of Visual representation at scAle
//! https://github.com/baaivision/EVA
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::Parser;
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{Module, Va... | candle/candle-examples/examples/eva2/main.rs/0 | {
"file_path": "candle/candle-examples/examples/eva2/main.rs",
"repo_id": "candle",
"token_count": 1221
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::qwen2::{Config, Model};
use candle::{DType, Tensor};
use candle_nn::VarBuilder;
use hf_hub::{api::sync::Api, Repo, Repo... | candle/candle-examples/examples/gte-qwen/main.rs/0 | {
"file_path": "candle/candle-examples/examples/gte-qwen/main.rs",
"repo_id": "candle",
"token_count": 2613
} |
# candle-llava
LLaVA (Large Language-and-Vision Assistant) is an end-to-end trained large
multimodal model. This example is from [candle-llava](https://github.com/chenwanqq/candle-llava)
The code is based on [https://github.com/haotian-liu/LLaVA](https://github.com/haotian-liu/LLaVA), Hence the llava-hf version of co... | candle/candle-examples/examples/llava/readme.md/0 | {
"file_path": "candle/candle-examples/examples/llava/readme.md",
"repo_id": "candle",
"token_count": 671
} |
# candle-mixtral: 8x7b LLM using a sparse mixture of experts.
Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters.
- [Blog post](https://mistral.ai/news/mixtral-of-experts/) from Mistral announcing the model release.
- [Model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the Hu... | candle/candle-examples/examples/mixtral/README.md/0 | {
"file_path": "candle/candle-examples/examples/mixtral/README.md",
"repo_id": "candle",
"token_count": 322
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use candle::{DType, IndexOp, Shape, Tensor, D};
use candle_nn::VarBuilder;
use candle_transformers::models::nvembed_v2::model::Model;
use clap::Parser;
use hf_hub::{api::sy... | candle/candle-examples/examples/nvembed_v2/main.rs/0 | {
"file_path": "candle/candle-examples/examples/nvembed_v2/main.rs",
"repo_id": "candle",
"token_count": 3339
} |
# candle-quantized-qwen2-instruct
[Qwen2]((https://qwenlm.github.io/blog/qwen2/)) is an upgraded version of Qwen1.5, released by Alibaba Cloud.
## Running the example
```bash
cargo run --example quantized-qwen2-instruct --release -- --prompt "Write a function to count prime numbers up to N."
```
0.5b, 1.5b, 7b and ... | candle/candle-examples/examples/quantized-qwen2-instruct/README.md/0 | {
"file_path": "candle/candle-examples/examples/quantized-qwen2-instruct/README.md",
"repo_id": "candle",
"token_count": 129
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::Result;
use clap::{Parser, Subcommand};
mod gym_env;
mod vec_gym_env;
mod ddpg;
mod dqn;
mod policy_gradient;
#[derive(Parser)]
struct Args {
#[command(subcommand)]
command: Command,
... | candle/candle-examples/examples/reinforcement-learning/main.rs/0 | {
"file_path": "candle/candle-examples/examples/reinforcement-learning/main.rs",
"repo_id": "candle",
"token_count": 277
} |
# candle-stable-diffusion: A Diffusers API in Rust/Candle
_A rusty robot holding a fire torch in its hand_, generated by Stable Diffusion
XL using Rust and [candle](https://github.com/huggingface/candle).
The `stable-diffusion` example is a conversion... | candle/candle-examples/examples/stable-diffusion/README.md/0 | {
"file_path": "candle/candle-examples/examples/stable-diffusion/README.md",
"repo_id": "candle",
"token_count": 935
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{ModuleT, VarBuilder};
use candle_transformers::models::vgg::{Models, Vgg};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Whic... | candle/candle-examples/examples/vgg/main.rs/0 | {
"file_path": "candle/candle-examples/examples/vgg/main.rs",
"repo_id": "candle",
"token_count": 967
} |
use std::path::PathBuf;
use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::models::xlm_roberta::{
Config, XLMRobertaForMaskedLM, XLMRobertaForSequenceClassification,
};
use clap::{Parser, ValueEnum};
use hf_hub::{api::sync::Api, Repo, RepoType};
use ... | candle/candle-examples/examples/xlm-roberta/main.rs/0 | {
"file_path": "candle/candle-examples/examples/xlm-roberta/main.rs",
"repo_id": "candle",
"token_count": 4653
} |
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <tuple>
#include <cstdio>
#if !defined(__CUDACC_RTC__)
#include "cuda_runtime.h"
#endif
#define CHECK... | candle/candle-flash-attn/kernels/hardware_info.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/hardware_info.h",
"repo_id": "candle",
"token_count": 854
} |
fn main() {
println!("cargo:rerun-if-changed=build.rs");
println!("cargo:rerun-if-changed=src/compatibility.cuh");
println!("cargo:rerun-if-changed=src/cuda_utils.cuh");
println!("cargo:rerun-if-changed=src/binary_op_macros.cuh");
let builder = bindgen_cuda::Builder::default();
println!("cargo:... | candle/candle-kernels/build.rs/0 | {
"file_path": "candle/candle-kernels/build.rs",
"repo_id": "candle",
"token_count": 177
} |
[package]
name = "candle-metal-kernels"
version = "0.8.2"
edition = "2021"
description = "Metal kernels for Candle"
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[dependencies]
metal = { version = "0.27.0"... | candle/candle-metal-kernels/Cargo.toml/0 | {
"file_path": "candle/candle-metal-kernels/Cargo.toml",
"repo_id": "candle",
"token_count": 259
} |
// Updated from MLX commit has f70764a
#include <metal_stdlib>
#include <metal_simdgroup>
using namespace metal;
// ============ "mlx/backend/metal/kernels/scaled_dot_product_attention_params.h"
struct MLXFastAttentionParams {
const int M;
const int N;
const int K;
const int ldq; // ldq == ldo
const int ... | candle/candle-metal-kernels/src/scaled_dot_product_attention.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/scaled_dot_product_attention.metal",
"repo_id": "candle",
"token_count": 21797
} |
use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle::{DType, Device, Module, Tensor};
use candle_nn::LayerNorm;
use criterion::{black_box, criterion_group, Criterion};
use std::time::Instant;
fn run(input: &Tensor, weight: &Tensor, bias: &Tensor) {
let _ = LayerNorm::new(weight.clone(), bias.clone... | candle/candle-nn/benches/benchmarks/layer_norm.rs/0 | {
"file_path": "candle/candle-nn/benches/benchmarks/layer_norm.rs",
"repo_id": "candle",
"token_count": 676
} |
//! Linear layer
//!
//! This layer applies a linear transformation to the incoming data, `y = x@w.t() + b`.
//! The bias is optional. The `forward` method can be used to apply the layer, it supports input
//! with a batch dimension (so of shape `(b_sz, in_c)`) or without (of shape `(in_c,)`), the
//! output has shape ... | candle/candle-nn/src/linear.rs/0 | {
"file_path": "candle/candle-nn/src/linear.rs",
"repo_id": "candle",
"token_count": 1252
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::test_utils::{to_vec0_round, to_vec2_round};
use anyhow::Result;
use candle::{DType, Device, Tensor, Var};
use candle_nn::{AdamW, Linear, Module, Optimizer, ParamsAdamW, SGD};
#[test]
fn sgd_op... | candle/candle-nn/tests/optim.rs/0 | {
"file_path": "candle/candle-nn/tests/optim.rs",
"repo_id": "candle",
"token_count": 2568
} |
from candle.utils import load_safetensors, save_gguf, load_gguf
from candle.models.bert import BertModel, Config
import json
from candle import Tensor
from tqdm import tqdm
from dataclasses import fields
import os
import time
from huggingface_hub import hf_hub_download
from transformers import BertTokenizer, AutoModel... | candle/candle-pyo3/e5.py/0 | {
"file_path": "candle/candle-pyo3/e5.py",
"repo_id": "candle",
"token_count": 1778
} |
import candle
from candle import Tensor
_UNSIGNED_DTYPES = set([str(candle.u8), str(candle.u32)])
def _assert_tensor_metadata(
actual: Tensor,
expected: Tensor,
check_device: bool = True,
check_dtype: bool = True,
check_layout: bool = True,
check_stride: bool = False,
):
if check_device:... | candle/candle-pyo3/py_src/candle/testing/__init__.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/testing/__init__.py",
"repo_id": "candle",
"token_count": 854
} |
import candle
from candle import Tensor
from candle.testing import assert_equal, assert_almost_equal
import pytest
@pytest.mark.parametrize("dtype", [candle.f32, candle.f64, candle.f16, candle.u32, candle.u8, candle.i64])
def test_assert_equal_asserts_correctly(dtype: candle.DType):
a = Tensor([1, 2, 3]).to(dtype... | candle/candle-pyo3/tests/bindings/test_testing.py/0 | {
"file_path": "candle/candle-pyo3/tests/bindings/test_testing.py",
"repo_id": "candle",
"token_count": 476
} |
//! Chinese contrastive Language-Image Pre-Training
//!
//! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - 💻 [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
//! - 💻 [HF](https://github.com/huggingface/transformers/blob/5af... | candle/candle-transformers/src/models/chinese_clip/text_model.rs/0 | {
"file_path": "candle/candle-transformers/src/models/chinese_clip/text_model.rs",
"repo_id": "candle",
"token_count": 8950
} |
//! EfficientViT (MSRA) inference implementation based on timm.
//!
//! This crate provides an implementation of the EfficientViT model from Microsoft Research Asia
//! for efficient image classification. The model uses cascaded group attention modules
//! to achieve strong performance while maintaining low memory usag... | candle/candle-transformers/src/models/efficientvit.rs/0 | {
"file_path": "candle/candle-transformers/src/models/efficientvit.rs",
"repo_id": "candle",
"token_count": 7414
} |
//! # JinaBERT inference implementation
//!
//! Based on implementation from huggingface for Jina BERT and its variants
//!
//! See: [Jina Embeddings on HuggingFace](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)
use super::with_tracing::{linear, linear_no_bias, Embedding, Linear};
use candle::{DType, Devic... | candle/candle-transformers/src/models/jina_bert.rs/0 | {
"file_path": "candle/candle-transformers/src/models/jina_bert.rs",
"repo_id": "candle",
"token_count": 6364
} |
//! NV-Embed-v2
//!
//! NV-Embed-v2 is a text embedding model that combines a Mistral decoder with a latent attention mechanism to produce high-quality text embeddings.
//!
//! This implementation is based on the [paper](https://arxiv.org/pdf/2405.17428) and [weights](https://huggingface.co/nvidia/NV-Embed-v2)
//!
//! ... | candle/candle-transformers/src/models/nvembed_v2/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/nvembed_v2/mod.rs",
"repo_id": "candle",
"token_count": 211
} |
//! Quantized Llama2 model implementation.
//!
//! This provides an 8-bit quantized implementation of Meta's LLaMA2 language model
//! for reduced memory usage and faster inference.
//!
//! Key characteristics:
//! - Decoder-only transformer architecture
//! - RoPE position embeddings
//! - Grouped Query Attention
//! ... | candle/candle-transformers/src/models/quantized_llama2_c.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_llama2_c.rs",
"repo_id": "candle",
"token_count": 4607
} |
//! Recurrent Gemma model implementation
//!
//! Recurrent Gemma is a version of the Gemma language model that incorporates recurrent memory.
//! This allows the model to maintain state between predictions and have longer-range memory.
//!
//! Key characteristics:
//! - Real-gated linear recurrent units (RGLRU)
//! - 1... | candle/candle-transformers/src/models/recurrent_gemma.rs/0 | {
"file_path": "candle/candle-transformers/src/models/recurrent_gemma.rs",
"repo_id": "candle",
"token_count": 12053
} |
//! # Denoising Diffusion Implicit Models
//!
//! The Denoising Diffusion Implicit Models (DDIM) is a simple scheduler
//! similar to Denoising Diffusion Probabilistic Models (DDPM). The DDPM
//! generative process is the reverse of a Markovian process, DDIM generalizes
//! this to non-Markovian guidance.
//!
//! Denoi... | candle/candle-transformers/src/models/stable_diffusion/ddim.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/ddim.rs",
"repo_id": "candle",
"token_count": 3954
} |
//! TrOCR model implementation.
//!
//! TrOCR is a Transformer-based OCR model that uses a Vision Transformer encoder
//! and a BART-like decoder for optical character recognition.
//!
//! Key characteristics:
//! - Vision Transformer encoder for image processing
//! - BART-style decoder for text generation
//! - Learn... | candle/candle-transformers/src/models/trocr.rs/0 | {
"file_path": "candle/candle-transformers/src/models/trocr.rs",
"repo_id": "candle",
"token_count": 8631
} |
//! Yi model implementation.
//!
//! This candle implementation uses a pre-trained Yi decoder-only large language model for inference.
//! The model was trained by 01.AI and follows a standard transformer architecture similar to LLaMA.
//!
//! Original code:
//! - 💻 [Yi Model](https://huggingface.co/01-ai/Yi-6B)
//! -... | candle/candle-transformers/src/models/yi.rs/0 | {
"file_path": "candle/candle-transformers/src/models/yi.rs",
"repo_id": "candle",
"token_count": 6426
} |
export async function getEmbeddings(
worker,
weightsURL,
tokenizerURL,
configURL,
modelID,
sentences,
updateStatus = null
) {
return new Promise((resolve, reject) => {
worker.postMessage({
weightsURL,
tokenizerURL,
configURL,
modelID,
sentences,
});
function mes... | candle/candle-wasm-examples/bert/utils.js/0 | {
"file_path": "candle/candle-wasm-examples/bert/utils.js",
"repo_id": "candle",
"token_count": 1250
} |
image:
repository: ghcr.io/huggingface
name: chat-ui
tag: 0.0.0-latest
pullPolicy: IfNotPresent
replicas: 3
domain: huggingface.co
networkPolicy:
enabled: false
allowedBlocks: []
service:
type: NodePort
annotations: { }
serviceAccount:
enabled: false
create: false
name: ""
automountServiceA... | chat-ui/chart/values.yaml/0 | {
"file_path": "chat-ui/chart/values.yaml",
"repo_id": "chat-ui",
"token_count": 392
} |
# Text Generation Inference (TGI)
| Feature | Available |
| --------------------------- | --------- |
| [Tools](../tools) | Yes\* |
| [Multimodal](../multimodal) | Yes\* |
\* Tools are only supported with the Cohere Command R+ model with the Xenova tokenizers. Please see the [Too... | chat-ui/docs/source/configuration/models/providers/tgi.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/providers/tgi.md",
"repo_id": "chat-ui",
"token_count": 1063
} |
<script lang="ts">
import { onDestroy } from "svelte";
import IconCopy from "./icons/IconCopy.svelte";
import Tooltip from "./Tooltip.svelte";
interface Props {
classNames?: string;
value: string;
children?: import("svelte").Snippet;
onClick?: () => void;
}
let { classNames = "", value, children, onCli... | chat-ui/src/lib/components/CopyToClipBoardBtn.svelte/0 | {
"file_path": "chat-ui/src/lib/components/CopyToClipBoardBtn.svelte",
"repo_id": "chat-ui",
"token_count": 620
} |
<script lang="ts">
import { run } from "svelte/legacy";
import { fade } from "svelte/transition";
import { onDestroy } from "svelte";
import IconChevron from "./icons/IconChevron.svelte";
let visible = $state(false);
interface Props {
scrollNode: HTMLElement;
class?: string;
}
let { scrollNode, class: cl... | chat-ui/src/lib/components/ScrollToBottomBtn.svelte/0 | {
"file_path": "chat-ui/src/lib/components/ScrollToBottomBtn.svelte",
"repo_id": "chat-ui",
"token_count": 518
} |
<script lang="ts">
import { env as envPublic } from "$env/dynamic/public";
import Logo from "$lib/components/icons/Logo.svelte";
import { createEventDispatcher } from "svelte";
import IconGear from "~icons/bi/gear-fill";
import AnnouncementBanner from "../AnnouncementBanner.svelte";
import type { Model } from "$l... | chat-ui/src/lib/components/chat/ChatIntroduction.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/ChatIntroduction.svelte",
"repo_id": "chat-ui",
"token_count": 1483
} |
<script lang="ts">
interface Props {
classNames?: string;
}
let { classNames = "" }: Props = $props();
</script>
<svg
xmlns="http://www.w3.org/2000/svg"
class={classNames}
width="1em"
height="1em"
fill="none"
viewBox="0 0 32 32"
><path
fill="currentColor"
fill-rule="evenodd"
d="M3.143 20.286h4.286v2... | chat-ui/src/lib/components/icons/IconNew.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconNew.svelte",
"repo_id": "chat-ui",
"token_count": 451
} |
import type { Migration } from ".";
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { logger } from "$lib/server/logger";
const addToolsToSettings: Migration = {
_id: new ObjectId("5c9c4c4c4c4c4c4c4c4c4c4c"),
name: "Add empty 'tools' record in settings",
up: async () =... | chat-ui/src/lib/migrations/routines/03-add-tools-in-settings.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/routines/03-add-tools-in-settings.ts",
"repo_id": "chat-ui",
"token_count": 272
} |
import { z } from "zod";
import type { EmbeddingEndpoint } from "../embeddingEndpoints";
import type { Tensor, FeatureExtractionPipeline } from "@huggingface/transformers";
import { pipeline } from "@huggingface/transformers";
export const embeddingEndpointTransformersJSParametersSchema = z.object({
weight: z.number(... | chat-ui/src/lib/server/embeddingEndpoints/transformersjs/embeddingEndpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/embeddingEndpoints/transformersjs/embeddingEndpoints.ts",
"repo_id": "chat-ui",
"token_count": 542
} |
import { buildPrompt } from "$lib/buildPrompt";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import type { Endpoint } from "../endpoints";
import { z } from "zod";
export const endpointOllamaParametersSchema = z.object({
weight: z.number().int().positive().default(1),
model: z.any(),
ty... | chat-ui/src/lib/server/endpoints/ollama/endpointOllama.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/ollama/endpointOllama.ts",
"repo_id": "chat-ui",
"token_count": 1380
} |
import type { ProcessedModel } from "../models";
import type { Endpoint } from "../endpoints/endpoints";
import type { Conversation } from "$lib/types/Conversation";
import type { Message } from "$lib/types/Message";
import type { Assistant } from "$lib/types/Assistant";
export interface TextGenerationContext {
model... | chat-ui/src/lib/server/textGeneration/types.ts/0 | {
"file_path": "chat-ui/src/lib/server/textGeneration/types.ts",
"repo_id": "chat-ui",
"token_count": 190
} |
/** Remove excess whitespace and newlines */
export const sanitizeString = (str: string) =>
str
.split("\n")
.map((s) => s.trim())
.filter(Boolean)
.join("\n")
.replaceAll(/ +/g, " ");
/** Collapses a string into a single line */
export const collapseString = (str: string) => sanitizeString(str.replaceAll(/... | chat-ui/src/lib/server/websearch/markdown/utils/nlp.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/markdown/utils/nlp.ts",
"repo_id": "chat-ui",
"token_count": 126
} |
import type { Message } from "$lib/types/Message";
import { format } from "date-fns";
import type { EndpointMessage } from "../../endpoints/endpoints";
import { generateFromDefaultEndpoint } from "../../generateFromDefaultEndpoint";
import { getReturnFromGenerator } from "$lib/utils/getReturnFromGenerator";
export asy... | chat-ui/src/lib/server/websearch/search/generateQuery.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/search/generateQuery.ts",
"repo_id": "chat-ui",
"token_count": 803
} |
import type { ObjectId } from "mongodb";
import type { Message } from "./Message";
import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
import type { Assistant } from "./Assistant";
export interface Conversation extends Timestamps {
_id: ObjectId;
sessionId?: string;
userId?: User["_... | chat-ui/src/lib/types/Conversation.ts/0 | {
"file_path": "chat-ui/src/lib/types/Conversation.ts",
"repo_id": "chat-ui",
"token_count": 182
} |
import type { ObjectId } from "mongodb";
import type { User } from "./User";
import type { Timestamps } from "./Timestamps";
import type { BackendToolContext } from "$lib/server/tools";
import type { MessageUpdate } from "./MessageUpdate";
import { z } from "zod";
import type { ReviewStatus } from "./Review";
export c... | chat-ui/src/lib/types/Tool.ts/0 | {
"file_path": "chat-ui/src/lib/types/Tool.ts",
"repo_id": "chat-ui",
"token_count": 1451
} |
<script lang="ts">
import { goto } from "$app/navigation";
import { base } from "$app/paths";
import { page } from "$app/state";
import { env as envPublic } from "$env/dynamic/public";
import ChatWindow from "$lib/components/chat/ChatWindow.svelte";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
imp... | chat-ui/src/routes/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/+page.svelte",
"repo_id": "chat-ui",
"token_count": 1058
} |
<script lang="ts">
import logo from "../../../../../static/huggingchat/logo.svg?raw";
interface Props {
name: string;
description?: string;
createdByName: string | undefined;
avatar: string | undefined;
}
let { name, description = "", createdByName, avatar }: Props = $props();
</script>
<div class="flex h... | chat-ui/src/routes/assistant/[assistantId]/thumbnail.png/ChatThumbnail.svelte/0 | {
"file_path": "chat-ui/src/routes/assistant/[assistantId]/thumbnail.png/ChatThumbnail.svelte",
"repo_id": "chat-ui",
"token_count": 573
} |
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