repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
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hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/models/test_santacoder.py | import pytest
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models.santacoder import SantaCoder
@pytest.fixture(scope="session")
def default_santacoder():
return SantaCoder("bigcode/santacoder")
@pytest.fixture
d... | 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/utils/test_tokens.py | import torch
from text_generation_server.utils.tokens import (
StopSequenceCriteria,
StoppingCriteria,
FinishReason,
batch_top_tokens,
)
def test_stop_sequence_criteria():
criteria = StopSequenceCriteria("/test;")
assert not criteria("/")
assert not criteria("/test")
assert criteria("... | 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/utils/test_convert.py | from text_generation_server.utils.hub import (
download_weights,
weight_hub_files,
weight_files,
)
from text_generation_server.utils.convert import convert_files
def test_convert_files():
model_id = "bigscience/bloom-560m"
pt_filenames = weight_hub_files(model_id, extension=".bin")
local_pt_f... | 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/utils/test_watermark.py | # test_watermark_logits_processor.py
import os
import numpy as np
import torch
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
DELTA = os.getenv("WATERMARK_DELTA", 2.0)
def test_seed_rng():
input_ids = [101, 2036, 3731, 102, 2003, 103]
p... | 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/utils/test_hub.py | import os
import requests
import tempfile
import pytest
import huggingface_hub.constants
from huggingface_hub import hf_api
import text_generation_server.utils.hub
from text_generation_server.utils.hub import (
weight_hub_files,
download_weights,
weight_files,
EntryNotFoundError,
LocalEntryNotFou... | 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/exllamav2_kernels/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="exllamav2_kernels",
ext_modules=[
CUDAExtension(
name="exllamav2_kernels",
sources=[
"exllamav2_kernels/ext.cpp",
"exllamav2_kernels/cuda... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h | #ifndef _config_h
#define _config_h
#define MAX_Q_GEMM_ROWS 50
#define MAX_Q_GEMM_WEIGHTS 4 // must be <= MAX_Q_GEMM_ROWS
#define QMODE_2BIT 1
#define QMODE_3BIT 1
#define QMODE_4BIT 1
#define QMODE_5BIT 1
#define QMODE_6BIT 0
#define QMODE_8BIT 0
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/ext.cpp | #include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include "config.h"
#include "cuda/q_matrix.cuh"
#include "cuda/q_gemm.cuh"
#include "cpp/util.h"
// Some decluttering macros
#define... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cpp/util.h | #ifndef _util_h
#define _util_h
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, __y) printf("%s, %s: %i, %i\n", #__x, #__y, __x, __y)
#define DBGI3(__x, __y, __z) printf("%s, %s, %s: %i, %i, %i\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGF(__x) printf("%s: %... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cuh | #ifndef _q_matrix_cuh
#define _q_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#define MAX_SUPERGROUPS 16
class QMatrix
{
public:
int device;
bool is_gptq;
int height;
int width;
int groups;
int gptq_groupsize;
int rows_8;
int rows... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/compat_gemm.cuh | #ifndef _compat_gemm_cuh
#define _compat_gemm_cuh
#if defined(USE_ROCM)
// For some reason this include is not present anywhere in exllama_v2 codebase, but it is required
// for symbols as hipblasHalf.
#include <hipblas/hipblas.h>
__host__ __forceinline__ hipblasStatus_t __compat_hipblasHgemm(hipblasHandle_t hand... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu | #include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define GPTQ_BLOCK_KN_SIZE 128
#define GPTQ_BLOCK_M_SIZE_MAX 8
#... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel.cuh | #include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result, const half qs_h)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hfm... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/compat.cuh | #ifndef _compat_cuh
#define _compat_cuh
// atomicAdd for half types, to support CC < 7.x
__device__ __forceinline__ void atomicAdd_half(half* address, half val)
{
unsigned int * address_as_ui = (unsigned int *) ((char *)address - ((size_t)address & 2));
unsigned int old = *address_as_ui;
unsigned int assu... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/matrix_view.cuh | #ifndef _matrix_view_cuh
#define _matrix_view_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include "quant/qdq_util.cuh"
class MatrixView_half
{
public:
const half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half(const half* data, const int height, const i... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cuh | #ifndef _q_gemm_cuh
#define _q_gemm_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q_matrix.cuh"
void gemm_half_q_half_cuda
(
cublasHandle_t cublas_handle,
const half* a,
QMatrix* b,
half* c,
int size_m,
i... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu | #include "q_matrix.cuh"
#include "matrix_view.cuh"
#include "util.cuh"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define BLOCK_KN_SIZE 128
#define THREADS_X 32
#define THREADS_Y 32
// Shuffle quan... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/util.cuh | #ifndef _util_cuh
#define _util_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#define DIVIDE(x, size) (((x) + (size) - 1) / (size))
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, _... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel_gptq.cuh | #include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hadd2(result, g_resu... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_4.cuh | #ifndef _qdq_4_cuh
#define _qdq_4_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_4BIT == 1
// Permutation:
//
// 77775555 33331111 66664444 22220000
__forceinline__ __device__ void shuffle_4bit_8
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unroll... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh | #ifndef _qdq_3_cuh
#define _qdq_3_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_3BIT == 1
// Permutation:
//
// v9997775 55333111 u8886664 44222000 (u, v lsb)
// vjjjhhhf ffdddbbb uiiiggge eecccaaa
// vtttrrrp ppnnnlll usssqqqo oommmkkk
__forceinline__ __device__ void shuffle_3bit_32
(
uin... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh | #ifndef _qdq_2_cuh
#define _qdq_2_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_2BIT == 1
// Permutation:
//
// ffddbb99 77553311 eeccaa88 66442200
__forceinline__ __device__ void shuffle_2bit_16
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unrol... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_8.cuh | #ifndef _qdq_8_cuh
#define _qdq_8_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_8BIT == 1
// Not implemented
#else
__forceinline__ __device__ void shuffle_8bit_4
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_8bit_8
(
const uint32_t q_0,
const uint32_t ... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh | #ifndef _qdq_util_cuh
#define _qdq_util_cuh
union half2_uint32
{
uint32_t as_uint32;
half2 as_half2;
__device__ half2_uint32(uint32_t val) : as_uint32(val) {}
__device__ half2_uint32(half2 val) : as_half2(val) {}
__device__ half2_uint32() : as_uint32(0) {}
};
union half_uint16
{
uint16_t as_ui... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_5.cuh | #ifndef _qdq_5_cuh
#define _qdq_5_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_5BIT == 1
// Permutation:
//
// v5555533 33311111 u4444422 22200000 (u, v lsb)
// vbbbbb99 99977777 uaaaaa88 88866666
// vhhhhhff fffddddd ugggggee eeeccccc
// vnnnnnll llljjjjj ummmmmkk kkkiiiii
// vtttttrr rrrppp... | 0 |
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda | hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_6.cuh | #ifndef _qdq_6_cuh
#define _qdq_6_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_6BIT == 1
// Not implemented
#else
__forceinline__ __device__ void shuffle_6bit_16
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_6bit_16
(
const uint32_t q_0,
const uint32_... | 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/custom_kernels/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import torch
extra_compile_args = ["-std=c++17"]
if not torch.version.hip:
extra_compile_args.append("-arch=compute_80")
setup(
name="custom_kernels",
ext_modules=[
CUDAExtension(
name="cus... | 0 |
hf_public_repos/text-generation-inference/server/custom_kernels | hf_public_repos/text-generation-inference/server/custom_kernels/custom_kernels/fused_bloom_attention_cuda.cu | #include <ATen/Dispatch.h>
#include <THC/THCAtomics.cuh>
#include <ATen/ATen.h>
#include <torch/torch.h>
#include <vector>
#include <optional>
/**
* Friendly reminder of how multithreading works in CUDA: https://developer.nvidia.com/blog/even-easier-introduction-cuda
* Check example at https://github.com/thomasw21/Li... | 0 |
hf_public_repos/text-generation-inference/server/custom_kernels | hf_public_repos/text-generation-inference/server/custom_kernels/custom_kernels/fused_attention_cuda.cu | #include <ATen/Dispatch.h>
#include <THC/THCAtomics.cuh>
#include <ATen/ATen.h>
#include <torch/torch.h>
#include <vector>
#include <optional>
/**
* Friendly reminder of how multithreading works in CUDA: https://developer.nvidia.com/blog/even-easier-introduction-cuda
* Check example at https://github.com/thomasw21/Li... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/docs/index.html | <html>
<head>
<!-- Load the latest Swagger UI code and style from npm using unpkg.com -->
<script src="https://unpkg.com/swagger-ui-dist@3/swagger-ui-bundle.js"></script>
<link rel="stylesheet" type="text/css" href="https://unpkg.com/swagger-ui-dist@3/swagger-ui.css"/>
<title>Text Ge... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/docs/openapi.json | {
"openapi": "3.0.3",
"info": {
"title": "Text Generation Inference",
"description": "Text Generation Webserver",
"contact": {
"name": "Olivier Dehaene"
},
"license": {
"name": "Apache 2.0",
"url": "https://www.apache.org/licenses/LICENSE-2.0"
},
"version": "1.3.4"
},... | 0 |
hf_public_repos/text-generation-inference/docs | hf_public_repos/text-generation-inference/docs/source/index.md | # Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
.
Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) model with TGI. Here is an exampl... | 0 |
hf_public_repos/text-generation-inference/docs | hf_public_repos/text-generation-inference/docs/source/installation.md | # Installation
This section explains how to install the CLI tool as well as installing TGI from source. **The strongly recommended approach is to use Docker, as it does not require much setup. Check [the Quick Tour](./quicktour) to learn how to run TGI with Docker.**
## Install CLI
You can use TGI command-line inter... | 0 |
hf_public_repos/text-generation-inference/docs | hf_public_repos/text-generation-inference/docs/source/_toctree.yml | - sections:
- local: index
title: Text Generation Inference
- local: quicktour
title: Quick Tour
- local: installation
title: Installation
- local: supported_models
title: Supported Models and Hardware
title: Getting started
- sections:
- local: basic_tutorials/consuming_tgi
title: Consu... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md | # Consuming Text Generation Inference
There are many ways you can consume Text Generation Inference server in your applications. After launching, you can use the `/generate` route and make a `POST` request to get results from the server. You can also use the `/generate_stream` route if you want TGI to return a stream ... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/basic_tutorials/non_core_models.md | # Non-core Model Serving
TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced b... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/basic_tutorials/preparing_model.md | # Preparing the Model
Text Generation Inference improves the model in several aspects.
## Quantization
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference wi... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/basic_tutorials/gated_model_access.md | # Serving Private & Gated Models
If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://hugging... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/basic_tutorials/using_cli.md | # Using TGI CLI
You can use TGI command-line interface (CLI) to download weights, serve and quantize models, or get information on serving parameters. To install the CLI, please refer to [the installation section](./installation#install-cli).
`text-generation-server` lets you download the model with `download-weights... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/basic_tutorials/launcher.md | # Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `Open... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/conceptual/quantization.md | # Quantization
TGI offers GPTQ and bits-and-bytes quantization to quantize large language models.
## Quantization with GPTQ
GPTQ is a post-training quantization method to make the model smaller. It quantizes the layers by finding a compressed version of that weight, that will yield a minimum mean squared error like ... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/conceptual/tensor_parallelism.md | # Tensor Parallelism
Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and the... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/conceptual/safetensors.md | # Safetensors
Safetensors is a model serialization format for deep learning models. It is [faster](https://huggingface.co/docs/safetensors/speed) and safer compared to other serialization formats like pickle (which is used under the hood in many deep learning libraries).
TGI depends on safetensors format mainly to e... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/conceptual/flash_attention.md | # Flash Attention
Scaling the transformer architecture is heavily bottlenecked by the self-attention mechanism, which has quadratic time and memory complexity. Recent developments in accelerator hardware mainly focus on enhancing compute capacities and not memory and transferring data between hardware. This results in... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/conceptual/streaming.md | # Streaming
## What is Streaming?
Token streaming is the mode in which the server returns the tokens one by one as the model generates them. This enables showing progressive generations to the user rather than waiting for the whole generation. Streaming is an essential aspect of the end-user experience as it reduces ... | 0 |
hf_public_repos/text-generation-inference/docs/source | hf_public_repos/text-generation-inference/docs/source/conceptual/paged_attention.md | # PagedAttention
LLMs struggle with memory limitations during generation. In the decoding part of generation, all the attention keys and values generated for previous tokens are stored in GPU memory for reuse. This is called _KV cache_, and it may take up a large amount of memory for large models and long sequences.
... | 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/README.md | # Text Generation
The Hugging Face Text Generation Python library provides a convenient way of interfacing with a
`text-generation-inference` instance running on
[Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) or on the Hugging Face Hub.
## Get Started
### Install
```shell
pip install... | 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/pyproject.toml | [tool.poetry]
name = "text-generation"
version = "0.6.1"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
readme = "README.md"
homepage = "https://github.com/huggingface/tex... | 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/poetry.lock | # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
version = "3.8.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
files = [
{file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_universal2.whl",... | 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/Makefile | unit-tests:
python -m pytest --cov=text_generation tests
install:
pip install pip --upgrade
pip install -e . | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/errors.py | from typing import Dict
# Text Generation Inference Errors
class ValidationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class GenerationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class OverloadedError(Exception):
def _... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/__init__.py | # 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... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/types.py | from enum import Enum
from pydantic import BaseModel, validator
from typing import Optional, List
from text_generation.errors import ValidationError
class Parameters(BaseModel):
# Activate logits sampling
do_sample: bool = False
# Maximum number of generated tokens
max_new_tokens: int = 20
# The ... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/inference_api.py | import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.e... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/client.py | import json
import requests
from aiohttp import ClientSession, ClientTimeout
from pydantic import ValidationError
from typing import Dict, Optional, List, AsyncIterator, Iterator
from text_generation.types import (
StreamResponse,
Response,
Request,
Parameters,
)
from text_generation.errors import par... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_types.py | import pytest
from text_generation.types import Parameters, Request
from text_generation.errors import ValidationError
def test_parameters_validation():
# Test best_of
Parameters(best_of=1)
with pytest.raises(ValidationError):
Parameters(best_of=0)
with pytest.raises(ValidationError):
... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_errors.py | from text_generation.errors import (
parse_error,
GenerationError,
IncompleteGenerationError,
OverloadedError,
ValidationError,
BadRequestError,
ShardNotReadyError,
ShardTimeoutError,
NotFoundError,
RateLimitExceededError,
UnknownError,
)
def test_generation_error():
pa... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/conftest.py | import pytest
from text_generation import __version__
from huggingface_hub.utils import build_hf_headers
@pytest.fixture
def flan_t5_xxl():
return "google/flan-t5-xxl"
@pytest.fixture
def fake_model():
return "fake/model"
@pytest.fixture
def unsupported_model():
return "gpt2"
@pytest.fixture
def ba... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_inference_api.py | import pytest
from text_generation import (
InferenceAPIClient,
InferenceAPIAsyncClient,
Client,
AsyncClient,
)
from text_generation.errors import NotSupportedError, NotFoundError
from text_generation.inference_api import check_model_support, deployed_models
def test_check_model_support(flan_t5_xxl, ... | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_client.py | import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason, InputToken
def test_generate(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate("t... | 0 |
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