Image-Text-to-Text
Transformers
Safetensors
mage_vl
multimodal
vision-language-model
mage-vl
video-understanding
streaming
conversational
custom_code
Instructions to use microsoft/Mage-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Mage-VL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="microsoft/Mage-VL", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("microsoft/Mage-VL", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use microsoft/Mage-VL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/Mage-VL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Mage-VL", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/microsoft/Mage-VL
- SGLang
How to use microsoft/Mage-VL with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "microsoft/Mage-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Mage-VL", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "microsoft/Mage-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Mage-VL", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use microsoft/Mage-VL with Docker Model Runner:
docker model run hf.co/microsoft/Mage-VL
File size: 17,735 Bytes
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*
* 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 to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* Rans64 extensions from:
* https://fgiesen.wordpress.com/2015/12/21/rans-in-practice/
* Unbounded range coding from:
* https://github.com/tensorflow/compression/blob/master/tensorflow_compression/cc/kernels/unbounded_index_range_coding_kernels.cc
**/
#include "rans.h"
#include <algorithm>
#include <cassert>
#include <cstring>
constexpr uint16_t bypass_precision = 2; /* number of bits in bypass mode */
constexpr uint16_t max_bypass_val = (1 << bypass_precision) - 1;
inline void RansEncPutBits(RansState& r, uint8_t*& ptr, uint32_t val)
{
RansAssert(bypass_precision <= 8);
RansAssert(val < (1u << bypass_precision));
constexpr uint32_t freq = 1 << (SCALE_BITS - bypass_precision);
constexpr uint32_t x_max = freq << ENC_RENORM_SHIFT_BITS;
while (r >= x_max) {
*(--ptr) = static_cast<uint8_t>(r & 0xff);
r >>= 8;
}
r = (r << bypass_precision) | val;
}
inline uint32_t RansDecGetBits(RansState& r, uint8_t*& ptr)
{
uint32_t val = r & ((1u << bypass_precision) - 1);
/* Re-normalize */
r = r >> bypass_precision;
if (r < RANS_BYTE_L) {
r = (r << 8) | *ptr++;
RansAssert(r >= RANS_BYTE_L);
}
return val;
}
RansEncoderLib::RansEncoderLib()
{
_stream = std::make_shared<std::vector<uint8_t>>();
}
int RansEncoderLib::add_cdf(const std::shared_ptr<std::vector<std::vector<int32_t>>> cdfs,
const std::shared_ptr<std::vector<int32_t>> cdfs_sizes,
const std::shared_ptr<std::vector<int32_t>> offsets)
{
auto ransSymbols = std::make_shared<std::vector<std::vector<RansSymbol>>>(cdfs->size());
for (int i = 0; i < static_cast<int>(cdfs->size()); i++) {
const int32_t* cdf = cdfs->at(i).data();
std::vector<RansSymbol> ransSym(cdfs->at(i).size());
const int ransSize = static_cast<int>(ransSym.size() - 1);
for (int j = 0; j < ransSize; j++) {
ransSym[j] = RansSymbol(
{ static_cast<uint16_t>(cdf[j]), static_cast<uint16_t>(cdf[j + 1] - cdf[j]) });
}
ransSymbols->at(i) = ransSym;
}
_ransSymbols.push_back(ransSymbols);
_cdfs_sizes.push_back(cdfs_sizes);
_offsets.push_back(offsets);
return static_cast<int>(_ransSymbols.size()) - 1;
}
void RansEncoderLib::empty_cdf_buffer()
{
_ransSymbols.clear();
_cdfs_sizes.clear();
_offsets.clear();
}
FORCE_INLINE void RansEncoderLib::encode_one_symbol(uint8_t*& ptr, RansState& rans, const int32_t symbol,
const int32_t cdf_size, const int32_t offset,
const std::vector<RansSymbol>& ransSymbols)
{
const int32_t max_value = cdf_size - 2;
int32_t value = symbol - offset;
uint32_t raw_val = 0;
if (value < 0) {
raw_val = -2 * value - 1;
value = max_value;
} else if (value >= max_value) {
raw_val = 2 * (value - max_value);
value = max_value;
}
if (value == max_value) {
std::vector<uint16_t> bypassBins;
bypassBins.reserve(20);
/* Determine the number of bypasses (in bypass_precision size) needed to
* encode the raw value. */
int32_t n_bypass = 0;
while ((raw_val >> (n_bypass * bypass_precision)) != 0) {
++n_bypass;
}
/* Encode number of bypasses */
int32_t val = n_bypass;
while (val >= max_bypass_val) {
bypassBins.push_back(max_bypass_val);
val -= max_bypass_val;
}
bypassBins.push_back(static_cast<uint16_t>(val));
/* Encode raw value */
for (int32_t j = 0; j < n_bypass; ++j) {
const int32_t val1 = (raw_val >> (j * bypass_precision)) & max_bypass_val;
bypassBins.push_back(static_cast<uint16_t>(val1));
}
for (auto it = bypassBins.rbegin(); it < bypassBins.rend(); it++) {
RansEncPutBits(rans, ptr, *it);
}
}
RansEncPut(rans, ptr, ransSymbols[value].start, ransSymbols[value].range);
}
void RansEncoderLib::encode_y(const std::shared_ptr<std::vector<int16_t>> symbols,
const int cdf_group_index)
{
PendingTask p;
p.workType = WorkType::EncodeDecodeY;
p.symbols_y = symbols;
p.cdf_group_index = cdf_group_index;
m_pendingEncodingList.push_back(p);
}
void RansEncoderLib::encode_z(const std::shared_ptr<std::vector<int8_t>> symbols,
const int cdf_group_index, const int start_offset,
const int per_channel_size)
{
PendingTask p;
p.workType = WorkType::EncodeDecodeZ;
p.symbols_z = symbols;
p.cdf_group_index = cdf_group_index;
p.start_offset = start_offset;
p.per_channel_size = per_channel_size;
m_pendingEncodingList.push_back(p);
}
#include <iostream>
FORCE_INLINE void RansEncoderLib::encode_y_internal(uint8_t*& ptr, RansState& rans,
const std::shared_ptr<std::vector<int16_t>> symbols,
const int cdf_group_index)
{
// backward loop on symbols from the end;
const int16_t* symbols_ptr = symbols->data();
const int32_t* cdfs_sizes_ptr = _cdfs_sizes[cdf_group_index]->data();
const int32_t* offsets_ptr = _offsets[cdf_group_index]->data();
const int symbol_size = static_cast<int>(symbols->size());
for (int i = symbol_size - 1; i >= 0; i--) {
const int32_t combined_symbol = symbols_ptr[i];
const int32_t cdf_idx = combined_symbol & 0xff;
const int32_t s = combined_symbol >> 8;
encode_one_symbol(ptr, rans, s, cdfs_sizes_ptr[cdf_idx], offsets_ptr[cdf_idx],
_ransSymbols[cdf_group_index]->at(cdf_idx));
}
}
FORCE_INLINE void RansEncoderLib::encode_z_internal(uint8_t*& ptr, RansState& rans,
const std::shared_ptr<std::vector<int8_t>> symbols,
const int cdf_group_index, const int start_offset,
const int per_channel_size)
{
// backward loop on symbols from the end;
const int8_t* symbols_ptr = symbols->data();
const int32_t* cdfs_sizes_ptr = _cdfs_sizes[cdf_group_index]->data();
const int32_t* offsets_ptr = _offsets[cdf_group_index]->data();
const int symbol_size = static_cast<int>(symbols->size());
for (int i = symbol_size - 1; i >= 0; i--) {
const int32_t cdf_idx = i / per_channel_size + start_offset;
encode_one_symbol(ptr, rans, symbols_ptr[i], cdfs_sizes_ptr[cdf_idx], offsets_ptr[cdf_idx],
_ransSymbols[cdf_group_index]->at(cdf_idx));
}
}
void RansEncoderLib::flush()
{
RansState rans;
RansEncInit(rans);
int32_t total_symbol_size = 0;
for (auto it = m_pendingEncodingList.begin(); it != m_pendingEncodingList.end(); it++) {
if (it->workType == WorkType::EncodeDecodeY) {
total_symbol_size += static_cast<int32_t>(it->symbols_y->size());
} else if (it->workType == WorkType::EncodeDecodeZ) {
total_symbol_size += static_cast<int32_t>(it->symbols_z->size());
}
}
if (total_symbol_size == 0) {
_stream->resize(0);
return;
}
uint8_t* output = new uint8_t[total_symbol_size]; // too much space ?
uint8_t* ptrEnd = output + total_symbol_size;
uint8_t* ptr = ptrEnd;
assert(ptr != nullptr);
for (auto it = m_pendingEncodingList.rbegin(); it != m_pendingEncodingList.rend(); it++) {
PendingTask p = *it;
if (p.workType == WorkType::EncodeDecodeY) {
encode_y_internal(ptr, rans, p.symbols_y, p.cdf_group_index);
} else if (p.workType == WorkType::EncodeDecodeZ) {
encode_z_internal(ptr, rans, p.symbols_z, p.cdf_group_index, p.start_offset,
p.per_channel_size);
}
}
RansEncFlush(rans, ptr);
const int nbytes = static_cast<int>(std::distance(ptr, ptrEnd));
_stream->resize(nbytes);
memcpy(_stream->data(), ptr, nbytes);
delete[] output;
}
std::shared_ptr<std::vector<uint8_t>> RansEncoderLib::get_encoded_stream()
{
return _stream;
}
void RansEncoderLib::reset()
{
m_pendingEncodingList.clear();
_stream->clear();
}
RansEncoderLibMultiThread::RansEncoderLibMultiThread()
: RansEncoderLib()
, m_finish(false)
, m_result_ready(false)
{
m_thread = std::thread(&RansEncoderLibMultiThread::worker, this);
}
RansEncoderLibMultiThread::~RansEncoderLibMultiThread()
{
{
std::lock_guard<std::mutex> lk(m_mutex_pending);
std::lock_guard<std::mutex> lk1(m_mutex_result);
m_finish = true;
}
m_cv_pending.notify_one();
m_cv_result.notify_one();
m_thread.join();
}
void RansEncoderLibMultiThread::flush()
{
PendingTask p;
p.workType = WorkType::Flush;
{
std::unique_lock<std::mutex> lk(m_mutex_pending);
m_pending.push_back(p);
}
m_cv_pending.notify_one();
}
std::shared_ptr<std::vector<uint8_t>> RansEncoderLibMultiThread::get_encoded_stream()
{
std::unique_lock<std::mutex> lk(m_mutex_result);
m_cv_result.wait(lk, [this] { return m_result_ready || m_finish; });
return RansEncoderLib::get_encoded_stream();
}
void RansEncoderLibMultiThread::reset()
{
RansEncoderLib::reset();
std::lock_guard<std::mutex> lk(m_mutex_result);
m_result_ready = false;
}
void RansEncoderLibMultiThread::worker()
{
while (!m_finish) {
std::unique_lock<std::mutex> lk(m_mutex_pending);
m_cv_pending.wait(lk, [this] { return m_pending.size() > 0 || m_finish; });
if (m_finish) {
lk.unlock();
break;
}
if (m_pending.size() == 0) {
lk.unlock();
// std::cout << "contine in worker" << std::endl;
continue;
}
while (m_pending.size() > 0) {
auto p = m_pending.front();
m_pending.pop_front();
lk.unlock();
if (p.workType == WorkType::Flush) {
RansEncoderLib::flush();
{
std::lock_guard<std::mutex> lk_result(m_mutex_result);
m_result_ready = true;
}
m_cv_result.notify_one();
}
lk.lock();
}
lk.unlock();
}
}
void RansDecoderLib::set_stream(const std::shared_ptr<std::vector<uint8_t>> encoded)
{
_stream = encoded;
_ptr8 = (uint8_t*)(_stream->data());
RansDecInit(_rans, _ptr8);
}
int RansDecoderLib::add_cdf(const std::shared_ptr<std::vector<std::vector<int32_t>>> cdfs,
const std::shared_ptr<std::vector<int32_t>> cdfs_sizes,
const std::shared_ptr<std::vector<int32_t>> offsets)
{
_cdfs.push_back(cdfs);
_cdfs_sizes.push_back(cdfs_sizes);
_offsets.push_back(offsets);
return static_cast<int>(_cdfs.size()) - 1;
}
void RansDecoderLib::empty_cdf_buffer()
{
_cdfs.clear();
_cdfs_sizes.clear();
_offsets.clear();
}
FORCE_INLINE int8_t RansDecoderLib::decode_one_symbol(const int32_t* cdf, const int32_t cdf_size,
const int32_t offset)
{
const int32_t max_value = cdf_size - 2;
const int32_t cum_freq = static_cast<int32_t>(RansDecGet(_rans));
int s = 1;
while (cdf[s++] <= cum_freq) {
}
s -= 2;
RansDecAdvance(_rans, _ptr8, cdf[s], cdf[s + 1] - cdf[s]);
int32_t value = static_cast<int32_t>(s);
if (value == max_value) {
/* Bypass decoding mode */
int32_t val = RansDecGetBits(_rans, _ptr8);
int32_t n_bypass = val;
while (val == max_bypass_val) {
val = RansDecGetBits(_rans, _ptr8);
n_bypass += val;
}
int32_t raw_val = 0;
for (int j = 0; j < n_bypass; ++j) {
val = RansDecGetBits(_rans, _ptr8);
raw_val |= val << (j * bypass_precision);
}
value = raw_val >> 1;
if (raw_val & 1) {
value = -value - 1;
} else {
value += max_value;
}
}
return static_cast<int8_t>(value + offset);
}
void RansDecoderLib::decode_y(const std::shared_ptr<std::vector<uint8_t>> indexes,
const int cdf_group_index)
{
int index_size = static_cast<int>(indexes->size());
m_decoded = std::make_shared<std::vector<int8_t>>(index_size);
int8_t* outout_ptr = m_decoded->data();
const uint8_t* indexes_ptr = indexes->data();
const int32_t* cdfs_sizes_ptr = _cdfs_sizes[cdf_group_index]->data();
const int32_t* offsets_ptr = _offsets[cdf_group_index]->data();
const auto& cdfs = _cdfs[cdf_group_index];
for (int i = 0; i < index_size; ++i) {
const int32_t cdf_idx = indexes_ptr[i];
outout_ptr[i] = decode_one_symbol(cdfs->at(cdf_idx).data(), cdfs_sizes_ptr[cdf_idx],
offsets_ptr[cdf_idx]);
}
}
void RansDecoderLib::decode_z(const int total_size, const int cdf_group_index,
const int start_offset, const int per_channel_size)
{
m_decoded = std::make_shared<std::vector<int8_t>>(total_size);
int8_t* outout_ptr = m_decoded->data();
const int32_t* cdfs_sizes_ptr = _cdfs_sizes[cdf_group_index]->data();
const int32_t* offsets_ptr = _offsets[cdf_group_index]->data();
const auto& cdfs = _cdfs[cdf_group_index];
for (int i = 0; i < total_size; ++i) {
const int32_t cdf_idx = i / per_channel_size + start_offset;
outout_ptr[i] = decode_one_symbol(cdfs->at(cdf_idx).data(), cdfs_sizes_ptr[cdf_idx],
offsets_ptr[cdf_idx]);
}
}
std::shared_ptr<std::vector<int8_t>> RansDecoderLib::get_decoded_tensor()
{
return m_decoded;
}
RansDecoderLibMultiThread::RansDecoderLibMultiThread()
: RansDecoderLib()
, m_finish(false)
, m_result_ready(false)
{
m_thread = std::thread(&RansDecoderLibMultiThread::worker, this);
}
RansDecoderLibMultiThread::~RansDecoderLibMultiThread()
{
{
std::lock_guard<std::mutex> lk(m_mutex_pending);
std::lock_guard<std::mutex> lk1(m_mutex_result);
m_finish = true;
}
m_cv_pending.notify_one();
m_cv_result.notify_one();
m_thread.join();
}
void RansDecoderLibMultiThread::decode_y(const std::shared_ptr<std::vector<uint8_t>> indexes,
const int cdf_group_index)
{
{
std::lock_guard<std::mutex> lk(m_mutex_result);
m_result_ready = false;
}
PendingTask p;
p.workType = WorkType::EncodeDecodeY;
p.indexes = indexes;
p.cdf_group_index = cdf_group_index;
{
std::unique_lock<std::mutex> lk(m_mutex_pending);
m_pending.push_back(p);
}
m_cv_pending.notify_one();
}
void RansDecoderLibMultiThread::decode_z(const int total_size, const int cdf_group_index,
const int start_offset, const int per_channel_size)
{
{
std::lock_guard<std::mutex> lk(m_mutex_result);
m_result_ready = false;
}
PendingTask p;
p.workType = WorkType::EncodeDecodeZ;
p.total_size = total_size;
p.cdf_group_index = cdf_group_index;
p.start_offset = start_offset;
p.per_channel_size = per_channel_size;
{
std::unique_lock<std::mutex> lk(m_mutex_pending);
m_pending.push_back(p);
}
m_cv_pending.notify_one();
}
std::shared_ptr<std::vector<int8_t>> RansDecoderLibMultiThread::get_decoded_tensor()
{
std::unique_lock<std::mutex> lk(m_mutex_result);
m_cv_result.wait(lk, [this] { return m_result_ready || m_finish; });
return RansDecoderLib::get_decoded_tensor();
}
void RansDecoderLibMultiThread::worker()
{
while (!m_finish) {
std::unique_lock<std::mutex> lk(m_mutex_pending);
m_cv_pending.wait(lk, [this] { return m_pending.size() > 0 || m_finish; });
if (m_finish) {
lk.unlock();
break;
}
if (m_pending.size() == 0) {
lk.unlock();
// std::cout << "contine in worker" << std::endl;
continue;
}
while (m_pending.size() > 0) {
auto p = m_pending.front();
m_pending.pop_front();
lk.unlock();
if (p.workType == WorkType::EncodeDecodeY) {
RansDecoderLib::decode_y(p.indexes, p.cdf_group_index);
} else if (p.workType == WorkType::EncodeDecodeZ) {
RansDecoderLib::decode_z(p.total_size, p.cdf_group_index, p.start_offset,
p.per_channel_size);
}
{
std::lock_guard<std::mutex> lk_result(m_mutex_result);
m_result_ready = true;
}
m_cv_result.notify_one();
lk.lock();
}
lk.unlock();
}
}
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