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Model: Josephgflowers/Phinance-Phi-3.5-mini-instruct-finance-v0.2 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- financial
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- fine-tuning
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- instruction-tuning
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- mini-LLM
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- finance-dataset
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- multi-turn-conversations
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- RAG
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- lightweight-finance-agent
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datasets:
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Josephgflowers/Phinance
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base_model: phi-3.5-mini-instruct
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model_type: instruct-LLM
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pipeline_tag: text-generation
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---
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# Model Card: Phinance-Phi-3.5-mini-instruct-finance-v0.2
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## Overview
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**Phinance-Phi-3.5-mini-instruct-finance-v0.2** is a fine-tuned mini language model specifically designed for financial tasks, instruction following, and multi-turn conversations. It leverages the **Phinance Dataset** to excel in finance-specific reasoning, question answering, and lightweight expert applications. The model is based on the **phi-3.5-mini** architecture, optimized for instruction-based workflows in the financial domain.
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### Key Features
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- **Finance-Focused Reasoning**: Handles complex tasks like portfolio analysis, market trends, and financial question answering.
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- **Instruction Following**: Trained for fine-grained instruction-based tasks within the financial sector.
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- **Multi-Turn Conversations**: Designed to handle context-aware dialogue with a focus on finance.
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- **RAG-Compatible**: Supports retrieval-augmented generation (RAG) through the use of data tokens (`<|data|>`) to integrate external data seamlessly.
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- **Lightweight Architecture**: Efficient for deployment on resource-constrained environments while maintaining robust performance.
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## Training Data
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The model was fine-tuned on the **Phinance Dataset**, a curated subset of financial content. The dataset includes multi-turn conversations formatted in **PHI style**, with financial relevance scored using advanced keyword matching.
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### Dataset Highlights:
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- **Topics**: Market trends, investment strategies, financial analysis, and more.
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- **Format**: Conversations in PHI format, including data tokens (`<|data|>`) for RAG use cases.
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- **Filtering**: High-quality finance-relevant content scored and selected using advanced methods.
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## Supported Tasks
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1. **Financial QA**: Answer complex questions about market analysis, financial terms, or investment strategies.
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2. **Multi-Turn Conversations**: Engage in context-aware dialogues about financial topics.
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3. **Instruction Following**: Execute finance-specific instructions and prompts with precision.
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4. **Lightweight Finance Domain Expert Agent**: Serve as an efficient, finance-focused assistant for lightweight systems.
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5. **Retrieval-Augmented Generation (RAG)**: Seamlessly integrate external data using the `<|data|>` token for enhanced responses.
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## Usage
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This model is ideal for:
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- Financial advisors or assistants
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- Chatbots and conversational agents
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- Financial QA systems
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- Lightweight domain-specific applications for finance
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### Help Here
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Like my work? Want to see more? Custom request? Message me on discord: joseph.flowers.ra Donate here: https://buymeacoffee.com/josephgflowers
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### How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Josephgflowers/Phinance-Phi-3.5-mini-instruct-finance-v0.2"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Example usage
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inputs = tokenizer("Explain the difference between stocks and bonds.", return_tensors="pt")
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outputs = model.generate(**inputs)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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Limitations and Considerations
|
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|
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Niche Knowledge: While proficient in financial topics, the model may not perform as well on general-purpose tasks.
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Bias: Data filtering may introduce biases toward certain financial sectors or topics.
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Hallucinations: As with any language model, responses should be verified for accuracy in critical applications.
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Model Details
|
||||
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Base Model: phi-3.5-mini
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Fine-Tuned Dataset: Phinance Dataset
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Version: v0.2
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Parameters: Mini-sized architecture for efficient performance
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Training Framework: Hugging Face Transformers
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License
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||||
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This model is licensed under the Apache 2.0 license.
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Citation
|
||||
|
||||
If you use this model, please cite:
|
||||
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@model{phinance_phi_3_5_mini_instruct_v0_2,
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title={Phinance-Phi-3.5-mini-instruct-finance-v0.2},
|
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author={Joseph G. Flowers},
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year={2025},
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url={https://huggingface.co/Josephgflowers/Phinance-Phi-3.5-mini-instruct-finance-v0.2}
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}
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added_tokens.json
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added_tokens.json
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{
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"<|assistant|>": 32001,
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"<|endoftext|>": 32000,
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config.json
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config.json
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{
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"_name_or_path": "./phinance",
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"architectures": [
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||||
"Phi3ForCausalLM"
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||||
],
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||||
"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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||||
"AutoConfig": "configuration_phi3.Phi3Config",
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||||
"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
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||||
},
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||||
"bos_token_id": 1,
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"embd_pdrop": 0.0,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"model_type": "phi3",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"original_max_position_embeddings": 4096,
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"pad_token_id": 32000,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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}
|
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1
configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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configuration_phi3.py
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configuration_phi3.py
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# coding=utf-8
|
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# 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",
|
||||
}
|
||||
|
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|
||||
class Phi3Config(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
|
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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).
|
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|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
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Args:
|
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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`.
|
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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 `longrope` 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_adjustment()
|
||||
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_adjustment(self):
|
||||
"""
|
||||
Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
|
||||
# For backward compatibility if previous version used "su" or "yarn"
|
||||
if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
|
||||
self.rope_scaling["type"] = "longrope"
|
||||
|
||||
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 ["longrope"]:
|
||||
raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], 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)}"
|
||||
)
|
||||
11
generation_config.json
Normal file
11
generation_config.json
Normal file
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": [
|
||||
32007,
|
||||
32001,
|
||||
32000
|
||||
],
|
||||
"pad_token_id": 32000,
|
||||
"transformers_version": "4.46.3"
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:023f26ca8c05cc79c5586b64b4774cc44cf2c5d0ee0b08cb4feca5f49e23e480
|
||||
size 4972489200
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:f8104e78f138536df8e6493869360ad233d36b160e01058bbc76921b8b00c378
|
||||
size 2669692488
|
||||
202
model.safetensors.index.json
Normal file
202
model.safetensors.index.json
Normal file
@@ -0,0 +1,202 @@
|
||||
{
|
||||
"metadata": {
|
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"total_size": 7642159104
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|
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1570
modeling_phi3.py
Normal file
1570
modeling_phi3.py
Normal file
File diff suppressed because it is too large
Load Diff
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
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|
||||
size 1844408
|
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3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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||||
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||||
132
tokenizer_config.json
Normal file
132
tokenizer_config.json
Normal file
@@ -0,0 +1,132 @@
|
||||
{
|
||||
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||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
}
|
||||
Reference in New Issue
Block a user