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---
language:
- en
pipeline_tag: text-generation
---
## 简介
cvx-coder 增强了大模型[CVX](https://cvxr.com/cvx) 代码能力和QA能力。它是[phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)在CVX文档, 合成代码, 论坛对话数据上的微调版本。
## 开始
先下载模型：
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/tommy1235/cvx-coder.git
```
然后运行下面示例代码
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
m_path="你的路径/cvx-coder"
model = AutoModelForCausalLM.from_pretrained(
m_path,
device_map="auto",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(m_path)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 2000,
"return_full_text": False,
"temperature": 0,
"do_sample": False,
}
content='''my problem is not convex, can i use cvx? if not, what should i do, be specific.'''
messages = [
{"role": "user", "content": content},
]
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
若想进入**聊天模式**，请运行下面的代码：
```python
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
m_path="你的路径/cvx-coder"
model = AutoModelForCausalLM.from_pretrained(
m_path,
device_map="auto",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(m_path)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 2000,
"return_full_text": False,
"temperature": 0,
"do_sample": False,
}
def assistant_talk(message, history):
message=[
{"role": "user", "content": message},
]
temp=[]
for i in history:
temp+=[{"role": "user", "content": i[0]},{"role": "assistant", "content": i[1]}]
messages =temp + message
output = pipe(messages, **generation_args)
return output[0]['generated_text']
gr.ChatInterface(assistant_talk).launch()
```

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{
"<|endoftext|>": 32000,
"<|assistant|>": 32001,
"<|placeholder1|>": 32002,
"<|placeholder2|>": 32003,
"<|placeholder3|>": 32004,
"<|placeholder4|>": 32005,
"<|system|>": 32006,
"<|end|>": 32007,
"<|placeholder5|>": 32008,
"<|placeholder6|>": 32009,
"<|user|>": 32010
}

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{
"_name_or_path": "/data/Phi-3-mini-4k-instruct",
"architectures": [
"Phi3ForCausalLM"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_phi3.Phi3Config",
"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
},
"bos_token_id": 1,
"embd_pdrop": 0.0,
"eos_token_id": 32000,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 4096,
"model_type": "phi3",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"original_max_position_embeddings": 4096,
"pad_token_id": 32000,
"resid_pdrop": 0.0,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 10000.0,
"sliding_window": 2047,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.41.2",
"use_cache": true,
"vocab_size": 32064
}

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{"framework":"pytorch","task":"text-generation","model":{"type":"cvx-coder2"},"pipeline":{"type":"cvx-coder-pipe"},"allow_remote":true}

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{"framework":"Pytorch","task":"text-generation"}

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# coding=utf-8
# Copyright 2024 Microsoft and 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 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.
""" Phi-3 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
}
class Phi3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the
[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32064):
Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Phi3Model`].
hidden_size (`int`, *optional*, defaults to 3072):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
resid_pdrop (`float`, *optional*, defaults to 0.0):
Dropout probability for mlp outputs.
embd_pdrop (`int`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio after computing the attention scores.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model was trained with. This is used to determine the size of the
original RoPE embeddings when using long scaling.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon value used for the RMSNorm.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`dict`, *optional*):
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
divided by the number of attention heads divided by 2.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 32000):
The id of the "end-of-sequence" token.
pad_token_id (`int`, *optional*, defaults to 32000):
The id of the padding token.
sliding_window (`int`, *optional*):
Sliding window attention window size. If `None`, no sliding window is applied.
Example:
```python
>>> from transformers import Phi3Model, Phi3Config
>>> # Initializing a Phi-3 style configuration
>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
>>> # Initializing a model from the configuration
>>> model = Phi3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "phi3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32064,
hidden_size=3072,
intermediate_size=8192,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="silu",
max_position_embeddings=4096,
original_max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
bos_token_id=1,
eos_token_id=32000,
pad_token_id=32000,
sliding_window=None,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.original_max_position_embeddings = original_max_position_embeddings
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.sliding_window = sliding_window
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
raise ValueError(
"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
if not (
isinstance(rope_scaling_short_factor, list)
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
):
raise ValueError(
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
)
if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
raise ValueError(
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
)
if not (
isinstance(rope_scaling_long_factor, list)
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
):
raise ValueError(
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
)
if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
raise ValueError(
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
)

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{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": [
32000,
32001,
32007
],
"pad_token_id": 32000,
"transformers_version": "4.41.2"
}

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}

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import os
import torch
from typing import Union, Dict, Any
from modelscope.pipelines.builder import PIPELINES
from modelscope.models.builder import MODELS
from modelscope.utils.constant import Tasks
from modelscope.pipelines.base import Pipeline
from modelscope.outputs import OutputKeys
from modelscope.pipelines.nlp.text_generation_pipeline import TextGenerationPipeline
from modelscope.models.base import Model, TorchModel
from modelscope.utils.logger import get_logger
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from transformers.generation.utils import GenerationConfig
import torch
from modelscope.models.base import TorchModel
from modelscope.preprocessors.base import Preprocessor
from modelscope.pipelines.base import Model, Pipeline
from modelscope.utils.config import Config
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors.builder import PREPROCESSORS
from modelscope.models.builder import MODELS
# m_path="你的路径/cvx-coder"
# model = AutoModelForCausalLM.from_pretrained(
# m_path,
# device_map="cuda",
# torch_dtype="auto",
# trust_remote_code=True,
# )
# tokenizer = AutoTokenizer.from_pretrained(m_path)
# pipe = pipeline(
# "text-generation",
# model=model,
# tokenizer=tokenizer,
# )
# generation_args = {
# "max_new_tokens": 2000,
# "return_full_text": False,
# "temperature": 0,
# "do_sample": False,
# }
# content='''my problem is not convex, can i use cvx? if not, what should i do, be specific.'''
# messages = [
# {"role": "user", "content": content},
# ]
# output = pipe(messages, **generation_args)
# print(output[0]['generated_text'])
@PIPELINES.register_module('text-generation', module_name='cvx-coder-pipe')
class Baichuan7BTextGenerationPipeline(TextGenerationPipeline):
def __init__(
self,
model: Union[Model, str],
*args,
**kwargs):
self.model = Baichuan7BTextGeneration(model) if isinstance(model, str) else model
super().__init__(model=model, **kwargs)
def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
return inputs
def _sanitize_parameters(self, **pipeline_parameters):
return {},pipeline_parameters,{}
# define the forward pass
def forward(self, inputs: str, **forward_params) -> Dict[str, Any]:
output = {}
content=inputs
messages = [
{"role": "user", "content": content},
]
outputs = self.model.pipeline(messages, **self.model.generation_args)
output['text'] = outputs[0]['generated_text']
return output
# format the outputs from pipeline
def postprocess(self, input, **kwargs) -> Dict[str, Any]:
return input
@MODELS.register_module('text-generation', module_name='cvx-coder')
class Baichuan7BTextGeneration(TorchModel):
def __init__(self, model_dir=None, *args, **kwargs):
super().__init__(model_dir, *args, **kwargs)
self.logger = get_logger()
# loading tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype="auto", trust_remote_code=True)
# self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",trust_remote_code=True)
self.model.generation_config = GenerationConfig.from_pretrained(model_dir)
self.model = self.model.eval()
from transformers import pipeline
self.pipeline=pipeline("text-generation",model=self.model,tokenizer=self.tokenizer,)
self.generation_args= {"max_new_tokens": 2000, "return_full_text": False,"temperature": 0, "do_sample": False,}
def forward(self, input: str, *args, **kwargs) -> Dict[str, Any]:
content = input
messages = [
{"role": "user", "content": content},
]
outputs = self.model.pipeline(messages, **self.model.generation_args)
response = outputs[0]['generated_text']
return {OutputKeys.RESPONSE:response, OutputKeys.HISTORY: ""}
def quantize(self, bits: int):
self.model = self.model.quantize(bits)
return self
def infer(self, input, **kwargs):
content = input
messages = [
{"role": "user", "content": content},
]
outputs = self.pipeline(messages, **self.model.generation_args)
response = outputs[0]['generated_text']
return response
# Tips: usr_config_path is the temporary save configuration location， after upload modelscope hub, it is the model_id
# usr_config_path = '/mnt/workspace/cvx-coder3'
usr_config_path = './'
config = Config({
"framework": 'pytorch',
"task": 'text-generation',
"model": {'type': 'cvx-coder'},
"pipeline": {"type": "cvx-coder-pipe"},
"allow_remote": True
})
config.dump('./'+ 'configuration.json')
if __name__ == "__main__":
from modelscope.models import Model
from modelscope.pipelines import pipeline
# model = Model.from_pretrained(usr_config_path)
input = "Hello, ModelScope!"
inference = pipeline('text-generation', model=usr_config_path)
output = inference(input)
print(output)

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{
"add_bos_token": true,
"add_eos_token": false,
"added_tokens_decoder": {
"0": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": false
},
"32000": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32001": {
"content": "<|assistant|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32002": {
"content": "<|placeholder1|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32003": {
"content": "<|placeholder2|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32004": {
"content": "<|placeholder3|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32005": {
"content": "<|placeholder4|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32006": {
"content": "<|system|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32007": {
"content": "<|end|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32008": {
"content": "<|placeholder5|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32009": {
"content": "<|placeholder6|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
},
"32010": {
"content": "<|user|>",
"lstrip": false,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": true
}
},
"bos_token": "<s>",
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"legacy": false,
"model_max_length": 4096,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": "<unk>",
"use_default_system_prompt": false
}