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Model: mohitskaushal/phi4-mini-inlegal-merged Source: Original Platform
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199
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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|
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#### Metrics
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|
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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|
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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12
added_tokens.json
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added_tokens.json
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{
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"<|/tool_call|>": 200026,
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"<|/tool|>": 200024,
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"<|assistant|>": 200019,
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"<|end|>": 200020,
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"<|system|>": 200022,
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"<|tag|>": 200028,
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"<|tool_call|>": 200025,
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"<|tool_response|>": 200027,
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"<|tool|>": 200023,
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"<|user|>": 200021
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}
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1
chat_template.jinja
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chat_template.jinja
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{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}
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143
config.json
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config.json
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{
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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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"AutoTokenizer": "Xenova/gpt-4o"
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},
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"bos_token_id": 199999,
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"embd_pdrop": 0.0,
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"eos_token_id": 199999,
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"full_attn_mod": 1,
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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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"interpolate_factor": 1,
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"lm_head_bias": false,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "phi3",
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"num_attention_heads": 24,
|
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
|
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"original_max_position_embeddings": 4096,
|
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"pad_token_id": 199999,
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"partial_rotary_factor": 0.75,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"long_factor": [
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1,
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1.118320672,
|
||||
1.250641126,
|
||||
1.398617824,
|
||||
1.564103225,
|
||||
1.74916897,
|
||||
1.956131817,
|
||||
2.187582649,
|
||||
2.446418898,
|
||||
2.735880826,
|
||||
3.059592084,
|
||||
3.421605075,
|
||||
3.826451687,
|
||||
4.279200023,
|
||||
4.785517845,
|
||||
5.351743533,
|
||||
5.984965424,
|
||||
6.693110555,
|
||||
7.485043894,
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||||
8.370679318,
|
||||
9.36110372,
|
||||
10.4687158,
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11.70738129,
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||||
13.09260651,
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14.64173252,
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||||
16.37415215,
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18.31155283,
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20.47818807,
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22.90118105,
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25.61086418,
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28.64115884,
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32.03,
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32.1,
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32.13,
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32.23,
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32.6,
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32.61,
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32.64,
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32.66,
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32.7,
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32.71,
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32.93,
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32.97,
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33.28,
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33.49,
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33.5,
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44.16,
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47.77
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],
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"short_factor": [
|
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1.0,
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],
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"type": "longrope"
|
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},
|
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"rope_theta": 10000.0,
|
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"sliding_window": 262144,
|
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"tie_word_embeddings": true,
|
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"torch_dtype": "float16",
|
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"transformers_version": "4.53.3",
|
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"use_cache": true,
|
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"vocab_size": 200064
|
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}
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226
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.
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#
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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.
|
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# 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.
|
||||
|
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"""Phi-3 model configuration"""
|
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class Phi3Config(PretrainedConfig):
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r"""
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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
|
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defaults will yield a similar configuration to that of the
|
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[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
|
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documentation from [`PretrainedConfig`] for more information.
|
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Args:
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vocab_size (`int`, *optional*, defaults to 32064):
|
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Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
|
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`inputs_ids` passed when calling [`Phi3Model`].
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hidden_size (`int`, *optional*, defaults to 3072):
|
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
|
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num_hidden_layers (`int`, *optional*, defaults to 32):
|
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Number of hidden layers in the Transformer decoder.
|
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num_attention_heads (`int`, *optional*, defaults to 32):
|
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Number of attention heads for each attention layer in the Transformer decoder.
|
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num_key_value_heads (`int`, *optional*):
|
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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
|
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`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
|
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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
|
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`num_attention_heads`.
|
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resid_pdrop (`float`, *optional*, defaults to 0.0):
|
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Dropout probability for mlp outputs.
|
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embd_pdrop (`int`, *optional*, defaults to 0.0):
|
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The dropout ratio for the embeddings.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio after computing the attention scores.
|
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
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max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model might ever be used with.
|
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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.
|
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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.
|
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
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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.
|
||||
partial_rotary_factor (`float`, *optional*, defaults to 1.0):
|
||||
Percentage of the query and keys which will have rotary embedding. Must be between 0.0 and 1.0.
|
||||
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,
|
||||
partial_rotary_factor=1.0,
|
||||
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.partial_rotary_factor = partial_rotary_factor
|
||||
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}"
|
||||
)
|
||||
rotary_ndims = int(self.hidden_size // self.num_attention_heads * self.partial_rotary_factor)
|
||||
if not len(rope_scaling_short_factor) == rotary_ndims // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must have length {rotary_ndims // 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) == rotary_ndims // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_long_factor)}"
|
||||
)
|
||||
10
generation_config.json
Normal file
10
generation_config.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 199999,
|
||||
"eos_token_id": [
|
||||
200020,
|
||||
199999
|
||||
],
|
||||
"pad_token_id": 199999,
|
||||
"transformers_version": "4.53.3"
|
||||
}
|
||||
199743
merges.txt
Normal file
199743
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
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:a5cbb1ec5d7b3c041881aed77869b71cb3b57902b43896a075a1fb021ebab57d
|
||||
size 3997603120
|
||||
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
|
||||
oid sha256:1f8eaf7ec9ad9bbceeac1cad117d059cab2d0e4b9427de51642319d1aaef9371
|
||||
size 3674462896
|
||||
202
model.safetensors.index.json
Normal file
202
model.safetensors.index.json
Normal file
@@ -0,0 +1,202 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 3836021760,
|
||||
"total_size": 7672043520
|
||||
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|
||||
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|
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1180
modeling_phi3.py
Normal file
1180
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
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|
||||
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:382cc235b56c725945e149cc25f191da667c836655efd0857b004320e90e91ea
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||||
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111
tokenizer_config.json
Normal file
111
tokenizer_config.json
Normal file
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|
||||
{
|
||||
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1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user