初始化项目，由ModelHub XC社区提供模型
Model: RedHatAI/Phi-3-medium-128k-instruct-quantized.w8a8 Source: Original Platform
This commit is contained in:
ModelHub XC
234
README.md
Normal file
234
README.md
Normal file
@@ -0,0 +1,234 @@
|
||||
---
|
||||
language:
|
||||
- en
|
||||
pipeline_tag: text-generation
|
||||
license: mit
|
||||
---
|
||||
|
||||
# Phi-3-medium-128k-instruct-quantized.w8a8
|
||||
|
||||
## Model Overview
|
||||
- **Model Architecture:** Phi-3
|
||||
- **Input:** Text
|
||||
- **Output:** Text
|
||||
- **Model Optimizations:**
|
||||
- **Activation quantization:** INT8
|
||||
- **Weight quantization:** INT8
|
||||
- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct), this models is intended for assistant-like chat.
|
||||
- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
|
||||
- **Release Date:** 7/11/2024
|
||||
- **Version:** 1.0
|
||||
- **License(s):** [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)
|
||||
- **Model Developers:** Neural Magic
|
||||
|
||||
Quantized version of [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct), a 14 billion-parameter open model trained using the Phi-3 datasets.
|
||||
It achieves an average score of 73.90 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 74.10.
|
||||
|
||||
### Model Optimizations
|
||||
|
||||
This model was obtained by quantizing the weights of [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) to INT8 data type.
|
||||
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
|
||||
Weight quantization also reduces disk size requirements by approximately 50%.
|
||||
|
||||
Only weights and activations of the linear operators within transformers blocks are quantized.
|
||||
Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
|
||||
Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
|
||||
Linear scaling factors are computed via by minimizing the mean squarred error (MSE).
|
||||
The [SmoothQuant](https://arxiv.org/abs/2211.10438) algorithm is used to alleviate outliers in the activations, whereas rhe [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization.
|
||||
Both algorithms are implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
|
||||
GPTQ used a 1% damping factor and 512 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
|
||||
|
||||
|
||||
## Deployment
|
||||
|
||||
### Use with vLLM
|
||||
|
||||
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below (using 2 GPUs).
|
||||
|
||||
```python
|
||||
from vllm import LLM, SamplingParams
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
model_id = "neuralmagic/Phi-3-medium-128k-instruct-quantized.w8a8"
|
||||
number_gpus = 2
|
||||
|
||||
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
|
||||
{"role": "user", "content": "Who are you?"},
|
||||
]
|
||||
|
||||
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
|
||||
|
||||
llm = LLM(model=model_id, trust_remote_code=True, max_model_len=8196, tensor_parallel_size=number_gpus)
|
||||
|
||||
outputs = llm.generate(prompts, sampling_params)
|
||||
|
||||
generated_text = outputs[0].outputs[0].text
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
|
||||
|
||||
|
||||
## Creation
|
||||
|
||||
This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer
|
||||
from datasets import Dataset
|
||||
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
|
||||
from llmcompressor.modifiers.quantization import GPTQModifier
|
||||
import random
|
||||
|
||||
model_id = "microsoft/Phi-3-medium-128k-instruct"
|
||||
|
||||
num_samples = 512
|
||||
max_seq_len = 8192
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
|
||||
def preprocess_fn(example):
|
||||
return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
|
||||
|
||||
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
|
||||
ds = ds.shuffle().select(range(num_samples))
|
||||
ds = ds.map(preprocess_fn)
|
||||
|
||||
recipe = [
|
||||
SmoothQuantModifier(
|
||||
smoothing_strength=0.8,
|
||||
mappings=[
|
||||
[["re:.*qkv_proj"], "re:.*input_layernorm"],
|
||||
[["re:.*gate_up_proj"], "re:.*post_attention_layernorm"],
|
||||
],
|
||||
),
|
||||
GPTQModifier(
|
||||
sequential=True,
|
||||
targets="Linear",
|
||||
scheme="W8A8",
|
||||
ignore=["lm_head"],
|
||||
dampening_frac=0.01,
|
||||
observer="mse",
|
||||
)
|
||||
]
|
||||
|
||||
model = SparseAutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
device_map="auto",
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
oneshot(
|
||||
model=model,
|
||||
dataset=ds,
|
||||
recipe=recipe,
|
||||
max_seq_length=max_seq_len,
|
||||
num_calibration_samples=num_samples,
|
||||
)
|
||||
|
||||
model.save_pretrained("Phi-3-medium-128k-instruct-quantized.w8a8")
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Evaluation
|
||||
|
||||
The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command (using 2 GPUs):
|
||||
```
|
||||
lm_eval \
|
||||
--model vllm \
|
||||
--model_args pretrained="neuralmagic/Phi-3-medium-128k-instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
|
||||
--tasks openllm \
|
||||
--batch_size auto
|
||||
```
|
||||
|
||||
### Accuracy
|
||||
|
||||
#### Open LLM Leaderboard evaluation scores
|
||||
<table>
|
||||
<tr>
|
||||
<td><strong>Benchmark</strong>
|
||||
</td>
|
||||
<td><strong>Phi-3-medium-128k-instruct </strong>
|
||||
</td>
|
||||
<td><strong>Phi-3-medium-128k-instruct-quantized.w8a8 (this model)</strong>
|
||||
</td>
|
||||
<td><strong>Recovery</strong>
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>MMLU (5-shot)
|
||||
</td>
|
||||
<td>76.69
|
||||
</td>
|
||||
<td>76.74
|
||||
</td>
|
||||
<td>100.1%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ARC Challenge (25-shot)
|
||||
</td>
|
||||
<td>69.45
|
||||
</td>
|
||||
<td>69.37
|
||||
</td>
|
||||
<td>99.9%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>GSM-8K (5-shot, strict-match)
|
||||
</td>
|
||||
<td>85.22
|
||||
</td>
|
||||
<td>84.15
|
||||
</td>
|
||||
<td>98.7%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Hellaswag (10-shot)
|
||||
</td>
|
||||
<td>85.10
|
||||
</td>
|
||||
<td>84.76
|
||||
</td>
|
||||
<td>99.6%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Winogrande (5-shot)
|
||||
</td>
|
||||
<td>73.56
|
||||
</td>
|
||||
<td>73.80
|
||||
</td>
|
||||
<td>100.3%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>TruthfulQA (0-shot)
|
||||
</td>
|
||||
<td>54.57
|
||||
</td>
|
||||
<td>54.57
|
||||
</td>
|
||||
<td>100.0%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Average</strong>
|
||||
</td>
|
||||
<td><strong>74.10</strong>
|
||||
</td>
|
||||
<td><strong>73.90</strong>
|
||||
</td>
|
||||
<td><strong>99.7%</strong>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
Reference in New Issue
Block a user