Upload OcisMllamaPipeline
Browse files- .gitattributes +1 -0
- README.md +199 -0
- config.json +310 -0
- configuration_ocismllama.py +117 -0
- generation_config.json +11 -0
- model-00001-of-00005.safetensors +3 -0
- model-00002-of-00005.safetensors +3 -0
- model-00003-of-00005.safetensors +3 -0
- model-00004-of-00005.safetensors +3 -0
- model-00005-of-00005.safetensors +3 -0
- model.safetensors.index.json +0 -0
- modeling_ocismllama.py +570 -0
- ocismllama_pipeline.py +134 -0
- special_tokens_map.json +23 -0
- tokenizer.json +3 -0
- tokenizer_config.json +2071 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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| 1 |
+
---
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library_name: transformers
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tags: []
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+
---
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| 5 |
+
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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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| 9 |
+
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| 10 |
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+
## Model Details
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+
### Model Description
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| 15 |
+
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+
<!-- Provide a longer summary of what this model is. -->
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| 17 |
+
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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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| 19 |
+
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+
- **Developed by:** [More Information Needed]
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| 21 |
+
- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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+
- **Model type:** [More Information Needed]
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| 24 |
+
- **Language(s) (NLP):** [More Information Needed]
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| 25 |
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- **License:** [More Information Needed]
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| 26 |
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- **Finetuned from model [optional]:** [More Information Needed]
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| 27 |
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### Model Sources [optional]
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+
<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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| 35 |
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| 36 |
+
## Uses
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| 37 |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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| 43 |
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[More Information Needed]
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| 45 |
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### Downstream Use [optional]
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| 47 |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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| 49 |
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[More Information Needed]
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| 51 |
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| 52 |
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### Out-of-Scope Use
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| 53 |
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| 54 |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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| 55 |
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[More Information Needed]
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| 57 |
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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| 63 |
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### Recommendations
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| 65 |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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| 67 |
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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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[More Information Needed]
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## Training Details
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| 77 |
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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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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## Model Examination [optional]
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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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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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "mllama-tp1-5epoch",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"OcisMllamaForConditionalGeneration"
|
| 5 |
+
],
|
| 6 |
+
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
+
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|
| 29 |
+
"0": "LABEL_0",
|
| 30 |
+
"1": "LABEL_1"
|
| 31 |
+
},
|
| 32 |
+
"input_hidden_size": 1280,
|
| 33 |
+
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|
| 34 |
+
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|
| 35 |
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|
| 36 |
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|
| 37 |
+
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|
| 38 |
+
},
|
| 39 |
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|
| 40 |
+
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|
| 41 |
+
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|
| 42 |
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|
| 43 |
+
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|
| 44 |
+
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|
| 45 |
+
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
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|
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
+
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|
| 75 |
+
},
|
| 76 |
+
"auto_map": {
|
| 77 |
+
"AutoConfig": "configuration_ocismllama.OcisMllamaConfig",
|
| 78 |
+
"AutoModel": "modeling_ocismllama.OcisMllamaForConditionalGeneration"
|
| 79 |
+
},
|
| 80 |
+
"custom_pipelines": {
|
| 81 |
+
"ocismllama-pipeline": {
|
| 82 |
+
"impl": "ocismllama_pipeline.OcisMllamaPipeline",
|
| 83 |
+
"pt": [
|
| 84 |
+
"AutoModel"
|
| 85 |
+
],
|
| 86 |
+
"tf": [],
|
| 87 |
+
"type": "multimodal"
|
| 88 |
+
}
|
| 89 |
+
},
|
| 90 |
+
"image_token_index": 128256,
|
| 91 |
+
"model_type": "ocismllama",
|
| 92 |
+
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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23,
|
| 108 |
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28,
|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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"eos_token_id": [
|
| 119 |
+
128001,
|
| 120 |
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128008,
|
| 121 |
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128009
|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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"id2label": {
|
| 130 |
+
"0": "LABEL_0",
|
| 131 |
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"1": "LABEL_1"
|
| 132 |
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},
|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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"label2id": {
|
| 138 |
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"LABEL_0": 0,
|
| 139 |
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|
| 140 |
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},
|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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"output_attentions": false,
|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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"return_dict": true,
|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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"factor": 8.0,
|
| 167 |
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|
| 168 |
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|
| 169 |
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"original_max_position_embeddings": 8192,
|
| 170 |
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"rope_type": "llama3"
|
| 171 |
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},
|
| 172 |
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|
| 173 |
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|
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|
| 175 |
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|
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|
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|
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|
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|
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|
| 181 |
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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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|
| 188 |
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|
| 189 |
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},
|
| 190 |
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|
| 191 |
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| 192 |
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|
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|
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|
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|
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|
| 216 |
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"0": "LABEL_0",
|
| 217 |
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|
| 218 |
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},
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|
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|
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|
| 222 |
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| 223 |
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|
| 224 |
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|
| 225 |
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|
| 226 |
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30
|
| 227 |
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|
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|
| 229 |
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|
| 230 |
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|
| 231 |
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|
| 233 |
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|
| 234 |
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|
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|
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| 238 |
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|
| 239 |
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|
| 240 |
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|
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|
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|
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|
| 244 |
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|
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|
| 247 |
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|
| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
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|
| 252 |
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|
| 253 |
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|
| 254 |
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|
| 255 |
+
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|
| 256 |
+
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|
| 257 |
+
"repetition_penalty": 1.0,
|
| 258 |
+
"return_dict": true,
|
| 259 |
+
"return_dict_in_generate": false,
|
| 260 |
+
"sep_token_id": null,
|
| 261 |
+
"supported_aspect_ratios": [
|
| 262 |
+
[
|
| 263 |
+
1,
|
| 264 |
+
1
|
| 265 |
+
],
|
| 266 |
+
[
|
| 267 |
+
1,
|
| 268 |
+
2
|
| 269 |
+
],
|
| 270 |
+
[
|
| 271 |
+
1,
|
| 272 |
+
3
|
| 273 |
+
],
|
| 274 |
+
[
|
| 275 |
+
1,
|
| 276 |
+
4
|
| 277 |
+
],
|
| 278 |
+
[
|
| 279 |
+
2,
|
| 280 |
+
1
|
| 281 |
+
],
|
| 282 |
+
[
|
| 283 |
+
2,
|
| 284 |
+
2
|
| 285 |
+
],
|
| 286 |
+
[
|
| 287 |
+
3,
|
| 288 |
+
1
|
| 289 |
+
],
|
| 290 |
+
[
|
| 291 |
+
4,
|
| 292 |
+
1
|
| 293 |
+
]
|
| 294 |
+
],
|
| 295 |
+
"suppress_tokens": null,
|
| 296 |
+
"task_specific_params": null,
|
| 297 |
+
"temperature": 1.0,
|
| 298 |
+
"tf_legacy_loss": false,
|
| 299 |
+
"tie_encoder_decoder": false,
|
| 300 |
+
"tie_word_embeddings": true,
|
| 301 |
+
"tokenizer_class": null,
|
| 302 |
+
"top_k": 50,
|
| 303 |
+
"top_p": 1.0,
|
| 304 |
+
"torch_dtype": "bfloat16",
|
| 305 |
+
"torchscript": false,
|
| 306 |
+
"typical_p": 1.0,
|
| 307 |
+
"use_bfloat16": false,
|
| 308 |
+
"vision_output_dim": 7680
|
| 309 |
+
}
|
| 310 |
+
}
|
configuration_ocismllama.py
ADDED
|
@@ -0,0 +1,117 @@
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
"""OcisMllama model configuration"""
|
| 3 |
+
|
| 4 |
+
from typing import Dict, List, Optional
|
| 5 |
+
|
| 6 |
+
from transformers import WhisperConfig
|
| 7 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 8 |
+
from transformers.modeling_rope_utils import rope_config_validation
|
| 9 |
+
from transformers.models.mllama.configuration_mllama import (
|
| 10 |
+
MllamaTextConfig, MllamaVisionConfig)
|
| 11 |
+
from transformers.utils import logging
|
| 12 |
+
|
| 13 |
+
logger = logging.get_logger(__name__)
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
class MllamaAudioConfig(PretrainedConfig):
|
| 17 |
+
model_type = "ocismllama"
|
| 18 |
+
|
| 19 |
+
def __init__(
|
| 20 |
+
self,
|
| 21 |
+
output_hidden_size: int = 4096,
|
| 22 |
+
hidden_size: int = 4096,
|
| 23 |
+
audio_model_id: str = 'AlexHung29629/whisper-large-v3-turbo-encoder',
|
| 24 |
+
stack_factor: int = 8,
|
| 25 |
+
norm_init: float = 0.4,
|
| 26 |
+
**kwargs,
|
| 27 |
+
):
|
| 28 |
+
self.output_hidden_size = output_hidden_size
|
| 29 |
+
self.hidden_size = hidden_size
|
| 30 |
+
self.stack_factor = stack_factor
|
| 31 |
+
self.norm_init = norm_init
|
| 32 |
+
self.audio_model_id = audio_model_id
|
| 33 |
+
whisper_config = WhisperConfig.from_pretrained(audio_model_id)
|
| 34 |
+
self.input_hidden_size = whisper_config.hidden_size
|
| 35 |
+
super().__init__(**kwargs)
|
| 36 |
+
|
| 37 |
+
class OcisMllamaConfig(PretrainedConfig):
|
| 38 |
+
r"""
|
| 39 |
+
This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
|
| 40 |
+
Mllama model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
| 41 |
+
with the defaults will yield a similar configuration to that of the Mllama-9B.
|
| 42 |
+
|
| 43 |
+
e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
|
| 44 |
+
|
| 45 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 46 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 47 |
+
|
| 48 |
+
Args:
|
| 49 |
+
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`):
|
| 50 |
+
The config object or dictionary of the vision backbone.
|
| 51 |
+
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`):
|
| 52 |
+
The config object or dictionary of the text backbone.
|
| 53 |
+
image_token_index (`int`, *optional*, defaults to 128256):
|
| 54 |
+
The image token index to encode the image prompt.
|
| 55 |
+
|
| 56 |
+
Example:
|
| 57 |
+
|
| 58 |
+
```python
|
| 59 |
+
>>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig
|
| 60 |
+
|
| 61 |
+
>>> # Initializing a CLIP-vision config
|
| 62 |
+
>>> vision_config = MllamaVisionConfig()
|
| 63 |
+
|
| 64 |
+
>>> # Initializing a Llama config
|
| 65 |
+
>>> text_config = MllamaTextConfig()
|
| 66 |
+
|
| 67 |
+
>>> # Initializing a mllama-11b style configuration
|
| 68 |
+
>>> configuration = MllamaConfig(vision_config, text_config)
|
| 69 |
+
|
| 70 |
+
>>> # Initializing a model from the mllama-11b style configuration
|
| 71 |
+
>>> model = MllamaForConditionalGeneration(configuration)
|
| 72 |
+
|
| 73 |
+
>>> # Accessing the model configuration
|
| 74 |
+
>>> configuration = model.config
|
| 75 |
+
```"""
|
| 76 |
+
|
| 77 |
+
model_type = "ocismllama"
|
| 78 |
+
sub_configs = {"vision_config": MllamaVisionConfig,
|
| 79 |
+
"text_config": MllamaTextConfig,
|
| 80 |
+
"audio_config": MllamaAudioConfig}
|
| 81 |
+
is_composition = True
|
| 82 |
+
|
| 83 |
+
def __init__(
|
| 84 |
+
self,
|
| 85 |
+
vision_config=None,
|
| 86 |
+
text_config=None,
|
| 87 |
+
audio_config=None,
|
| 88 |
+
image_token_index=128256,
|
| 89 |
+
**kwargs,
|
| 90 |
+
):
|
| 91 |
+
if vision_config is None:
|
| 92 |
+
self.vision_config = MllamaVisionConfig()
|
| 93 |
+
logger.info("vision_config is None, using default mllama vision config")
|
| 94 |
+
elif isinstance(vision_config, dict):
|
| 95 |
+
self.vision_config = MllamaVisionConfig(**vision_config)
|
| 96 |
+
elif isinstance(vision_config, MllamaVisionConfig):
|
| 97 |
+
self.vision_config = vision_config
|
| 98 |
+
|
| 99 |
+
self.image_token_index = image_token_index
|
| 100 |
+
|
| 101 |
+
if text_config is None:
|
| 102 |
+
self.text_config = MllamaTextConfig()
|
| 103 |
+
logger.info("text_config is None, using default mllama text config")
|
| 104 |
+
elif isinstance(text_config, dict):
|
| 105 |
+
self.text_config = MllamaTextConfig(**text_config)
|
| 106 |
+
elif isinstance(text_config, MllamaTextConfig):
|
| 107 |
+
self.text_config = text_config
|
| 108 |
+
|
| 109 |
+
if audio_config is None:
|
| 110 |
+
self.audio_config = MllamaAudioConfig(output_hidden_size=self.text_config.hidden_size)
|
| 111 |
+
logger.info("audio_config is None, using default mllama audio config")
|
| 112 |
+
elif isinstance(audio_config, dict):
|
| 113 |
+
self.audio_config = MllamaAudioConfig(**audio_config)
|
| 114 |
+
elif isinstance(audio_config, MllamaAudioConfig):
|
| 115 |
+
self.audio_config = audio_config
|
| 116 |
+
|
| 117 |
+
super().__init__(**kwargs)
|
generation_config.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 128000,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
128001,
|
| 6 |
+
128008,
|
| 7 |
+
128009
|
| 8 |
+
],
|
| 9 |
+
"pad_token_id": 128004,
|
| 10 |
+
"transformers_version": "4.46.2"
|
| 11 |
+
}
|
model-00001-of-00005.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f62552fe1a2a65deebd06869fc37a97ed17241e54ce6067cb2204eac42b1e673
|
| 3 |
+
size 4992622346
|
model-00002-of-00005.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5851419e869295865c1bf90e793514725c3c4106c6e36052d38adb8f37dad1f4
|
| 3 |
+
size 4966251712
|
model-00003-of-00005.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7bc818319560d20a38340287c50207f879610cb5061fce9cd35430c91178cd8c
|
| 3 |
+
size 4915919704
|
model-00004-of-00005.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3a22cc85f7165f7d17a1d1ffd728a6c47b3e43cdb4973321a269211901d7e4a0
|
| 3 |
+
size 4999823980
|
model-00005-of-00005.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1632e0aed5cc221bc9b2f17d05b9f95049e79f2b18681067968769ff5b559512
|
| 3 |
+
size 2840629504
|
model.safetensors.index.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
modeling_ocismllama.py
ADDED
|
@@ -0,0 +1,570 @@
|
|
|
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|
|
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|
| 1 |
+
"""
|
| 2 |
+
MIT License
|
| 3 |
+
|
| 4 |
+
Copyright (c) 2023 Fixie.ai
|
| 5 |
+
2024 Alex Hung
|
| 6 |
+
|
| 7 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 8 |
+
of this software and associated documentation files (the "Software"), to deal
|
| 9 |
+
in the Software without restriction, including without limitation the rights
|
| 10 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 11 |
+
copies of the Software, and to permit persons to whom the Software is
|
| 12 |
+
furnished to do so, subject to the following conditions:
|
| 13 |
+
|
| 14 |
+
The above copyright notice and this permission notice shall be included in all
|
| 15 |
+
copies or substantial portions of the Software.
|
| 16 |
+
|
| 17 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 18 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 19 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 20 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 21 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 22 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 23 |
+
SOFTWARE.
|
| 24 |
+
"""
|
| 25 |
+
|
| 26 |
+
from typing import List, Optional, Tuple, Union
|
| 27 |
+
|
| 28 |
+
import torch
|
| 29 |
+
import torch.nn.functional as F
|
| 30 |
+
import torch.utils.checkpoint
|
| 31 |
+
from torch import nn
|
| 32 |
+
from transformers import AutoConfig, AutoModel, WhisperConfig
|
| 33 |
+
from transformers.generation import GenerationMixin
|
| 34 |
+
from transformers.modeling_outputs import (BaseModelOutput,
|
| 35 |
+
CausalLMOutputWithPast)
|
| 36 |
+
from transformers.modeling_utils import ModuleUtilsMixin
|
| 37 |
+
from transformers.models.llama.modeling_llama import LlamaRMSNorm
|
| 38 |
+
from transformers.models.mllama.modeling_mllama import (
|
| 39 |
+
MllamaForCausalLM, MllamaPreTrainedModel, MllamaVisionModel,
|
| 40 |
+
_prepare_cross_attention_mask)
|
| 41 |
+
from transformers.models.whisper.modeling_whisper import WhisperEncoder
|
| 42 |
+
from transformers.utils import logging
|
| 43 |
+
|
| 44 |
+
from configuration_ocismllama import MllamaAudioConfig, OcisMllamaConfig
|
| 45 |
+
|
| 46 |
+
logger = logging.get_logger(__name__)
|
| 47 |
+
|
| 48 |
+
class OcisMllamaPreTrainedModel(MllamaPreTrainedModel):
|
| 49 |
+
config_class = OcisMllamaConfig
|
| 50 |
+
base_model_prefix = "model"
|
| 51 |
+
supports_gradient_checkpointing = True
|
| 52 |
+
_no_split_modules = [
|
| 53 |
+
"MllamaVisionEncoderLayer",
|
| 54 |
+
"MllamaCrossAttentionDecoderLayer",
|
| 55 |
+
"MllamaSelfAttentionDecoderLayer",
|
| 56 |
+
"WhisperEncoderLayer",
|
| 57 |
+
"WhisperDecoderLayer",
|
| 58 |
+
]
|
| 59 |
+
_supports_cache_class = True
|
| 60 |
+
_supports_static_cache = False # static cache cannot have different shapes for each layer
|
| 61 |
+
_supports_sdpa = True
|
| 62 |
+
_supports_quantized_cache = True
|
| 63 |
+
|
| 64 |
+
class OcisMllamaForConditionalGeneration(OcisMllamaPreTrainedModel, GenerationMixin):
|
| 65 |
+
_supports_quantized_cache = False # quant cache not supported in encoder-decoder setting
|
| 66 |
+
|
| 67 |
+
def __init__(self, config: OcisMllamaConfig):
|
| 68 |
+
super().__init__(config)
|
| 69 |
+
self.vocab_size = config.text_config.vocab_size
|
| 70 |
+
self.hidden_size = config.text_config.hidden_size
|
| 71 |
+
self.max_num_tiles = config.vision_config.max_num_tiles
|
| 72 |
+
self.vision_output_dim = config.vision_config.vision_output_dim
|
| 73 |
+
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
| 74 |
+
|
| 75 |
+
self.vision_model = MllamaVisionModel._from_config(config.vision_config)
|
| 76 |
+
self.language_model = MllamaForCausalLM._from_config(config.text_config)
|
| 77 |
+
self.multi_modal_projector = nn.Linear(
|
| 78 |
+
config.vision_config.vision_output_dim,
|
| 79 |
+
config.text_config.hidden_size,
|
| 80 |
+
bias=True,
|
| 81 |
+
)
|
| 82 |
+
whisper_config = WhisperConfig.from_pretrained(config.audio_config.audio_model_id)
|
| 83 |
+
self.audio_model = ModifiedWhisperEncoder._from_config(whisper_config)
|
| 84 |
+
self.audio_projector = UltravoxProjector(config.audio_config)
|
| 85 |
+
self.post_init()
|
| 86 |
+
|
| 87 |
+
def get_input_embeddings(self):
|
| 88 |
+
return self.language_model.get_input_embeddings()
|
| 89 |
+
|
| 90 |
+
def set_input_embeddings(self, value):
|
| 91 |
+
self.language_model.set_input_embeddings(value)
|
| 92 |
+
|
| 93 |
+
def get_output_embeddings(self):
|
| 94 |
+
return self.language_model.get_output_embeddings()
|
| 95 |
+
|
| 96 |
+
def set_output_embeddings(self, new_embeddings):
|
| 97 |
+
self.language_model.set_output_embeddings(new_embeddings)
|
| 98 |
+
|
| 99 |
+
def set_decoder(self, decoder):
|
| 100 |
+
self.language_model.set_decoder(decoder)
|
| 101 |
+
|
| 102 |
+
def get_decoder(self):
|
| 103 |
+
return self.language_model.get_decoder()
|
| 104 |
+
|
| 105 |
+
def tie_weights(self):
|
| 106 |
+
return self.language_model.tie_weights()
|
| 107 |
+
|
| 108 |
+
def forward(
|
| 109 |
+
self,
|
| 110 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 111 |
+
audio_values: Optional[torch.FloatTensor] = None,
|
| 112 |
+
audio_token_start_idx: Optional[torch.Tensor] = None,
|
| 113 |
+
audio_len: Optional[torch.Tensor] = None,
|
| 114 |
+
audio_token_len: Optional[torch.Tensor] = None,
|
| 115 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
| 116 |
+
aspect_ratio_mask: Optional[torch.Tensor] = None,
|
| 117 |
+
aspect_ratio_ids: Optional[torch.Tensor] = None,
|
| 118 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 119 |
+
cross_attention_mask: Optional[torch.Tensor] = None,
|
| 120 |
+
cross_attention_states: Optional[torch.Tensor] = None,
|
| 121 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 122 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 123 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 124 |
+
labels: Optional[torch.LongTensor] = None,
|
| 125 |
+
use_cache: Optional[bool] = None,
|
| 126 |
+
output_attentions: Optional[bool] = None,
|
| 127 |
+
output_hidden_states: Optional[bool] = None,
|
| 128 |
+
return_dict: Optional[bool] = None,
|
| 129 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 130 |
+
num_logits_to_keep: int = 0,
|
| 131 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 132 |
+
r"""
|
| 133 |
+
Args:
|
| 134 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 135 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 136 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 137 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 138 |
+
|
| 139 |
+
num_logits_to_keep (`int`, *optional*):
|
| 140 |
+
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
|
| 141 |
+
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
|
| 142 |
+
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
|
| 143 |
+
|
| 144 |
+
|
| 145 |
+
Returns:
|
| 146 |
+
|
| 147 |
+
Example:
|
| 148 |
+
|
| 149 |
+
```python
|
| 150 |
+
>>> from PIL import Image
|
| 151 |
+
>>> import requests
|
| 152 |
+
>>> from transformers import AutoProcessor, MllamaForConditionalGeneration
|
| 153 |
+
|
| 154 |
+
>>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
|
| 155 |
+
>>> model = MllamaForConditionalGeneration.from_pretrained(checkpoint)
|
| 156 |
+
>>> processor = AutoProcessor.from_pretrained(checkpoint)
|
| 157 |
+
|
| 158 |
+
>>> prompt = "<|image|>If I had to write a haiku for this one"
|
| 159 |
+
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
| 160 |
+
>>> image = Image.open(requests.get(url, stream=True).raw)
|
| 161 |
+
|
| 162 |
+
>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
|
| 163 |
+
|
| 164 |
+
>>> # Generate
|
| 165 |
+
>>> output = model.generate(**inputs, max_new_tokens=15)
|
| 166 |
+
|
| 167 |
+
>>> prompt_len = inputs.input_ids.shape[-1]
|
| 168 |
+
>>> generated_ids = output[:, prompt_len:]
|
| 169 |
+
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
| 170 |
+
>>> print(generated_text)
|
| 171 |
+
[', it would be:.\\nA stop sign in Chinatown.\\n']
|
| 172 |
+
```
|
| 173 |
+
"""
|
| 174 |
+
if cache_position[0] > 0:
|
| 175 |
+
audio_values = None
|
| 176 |
+
pixel_values = None
|
| 177 |
+
cross_attention_mask = None
|
| 178 |
+
|
| 179 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 180 |
+
output_hidden_states = (
|
| 181 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 182 |
+
)
|
| 183 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 184 |
+
|
| 185 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
| 186 |
+
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
| 187 |
+
|
| 188 |
+
if pixel_values is not None and inputs_embeds is not None:
|
| 189 |
+
raise ValueError(
|
| 190 |
+
"You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
|
| 191 |
+
)
|
| 192 |
+
|
| 193 |
+
if pixel_values is not None and cross_attention_states is not None:
|
| 194 |
+
raise ValueError("`pixel_values` and `cross_attention_states` cannot be provided simultaneously")
|
| 195 |
+
|
| 196 |
+
if pixel_values is not None:
|
| 197 |
+
if aspect_ratio_ids is None:
|
| 198 |
+
raise ValueError("`aspect_ratio_ids` must be provided if `pixel_values` is provided")
|
| 199 |
+
# get vision tokens from vision model
|
| 200 |
+
vision_outputs = self.vision_model(
|
| 201 |
+
pixel_values=pixel_values,
|
| 202 |
+
aspect_ratio_ids=aspect_ratio_ids,
|
| 203 |
+
aspect_ratio_mask=aspect_ratio_mask,
|
| 204 |
+
output_hidden_states=output_hidden_states,
|
| 205 |
+
output_attentions=output_attentions,
|
| 206 |
+
return_dict=return_dict,
|
| 207 |
+
)
|
| 208 |
+
cross_attention_states = vision_outputs[0]
|
| 209 |
+
cross_attention_states = self.multi_modal_projector(cross_attention_states).reshape(
|
| 210 |
+
-1, cross_attention_states.shape[-2], self.hidden_size
|
| 211 |
+
)
|
| 212 |
+
|
| 213 |
+
if cross_attention_mask is not None:
|
| 214 |
+
cross_attention_mask, full_text_row_masked_out_mask = _prepare_cross_attention_mask(
|
| 215 |
+
cross_attention_mask,
|
| 216 |
+
num_vision_tokens=self.vision_model.num_patches,
|
| 217 |
+
dtype=self.dtype,
|
| 218 |
+
)
|
| 219 |
+
else:
|
| 220 |
+
full_text_row_masked_out_mask = None
|
| 221 |
+
|
| 222 |
+
if cross_attention_mask is not None and cache_position is not None:
|
| 223 |
+
cross_attention_mask = cross_attention_mask[:, :, cache_position]
|
| 224 |
+
full_text_row_masked_out_mask = full_text_row_masked_out_mask[:, :, cache_position]
|
| 225 |
+
|
| 226 |
+
if audio_values is not None:
|
| 227 |
+
inputs_embeds = self.get_input_embeddings().forward(input_ids)
|
| 228 |
+
assert (
|
| 229 |
+
audio_token_start_idx is not None and audio_token_len is not None
|
| 230 |
+
), "audio_token_start_idx and audio_token_len must be provided if audio_values are provided."
|
| 231 |
+
assert (
|
| 232 |
+
len(audio_token_start_idx) == len(audio_token_len) == len(audio_values)
|
| 233 |
+
), "audio_token_start_idx, audio_token_len, and audio_values must have the same batch size."
|
| 234 |
+
|
| 235 |
+
# B x A/3200 x D
|
| 236 |
+
audio_tower_output = self.audio_model.forward(
|
| 237 |
+
audio_values.to(self.audio_model.dtype),
|
| 238 |
+
audio_len = audio_len
|
| 239 |
+
).last_hidden_state
|
| 240 |
+
audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)
|
| 241 |
+
|
| 242 |
+
audio_embeds = self.audio_projector.forward(audio_tower_output)
|
| 243 |
+
|
| 244 |
+
# combine audio and text embeddings
|
| 245 |
+
for i, (audio, start, length) in enumerate(
|
| 246 |
+
zip(audio_embeds, audio_token_start_idx, audio_token_len)
|
| 247 |
+
):
|
| 248 |
+
assert length <= audio.shape[0]
|
| 249 |
+
inputs_embeds[i, start : start + length].copy_(audio[:length])
|
| 250 |
+
input_ids = None
|
| 251 |
+
|
| 252 |
+
outputs = self.language_model(
|
| 253 |
+
input_ids=input_ids,
|
| 254 |
+
attention_mask=attention_mask,
|
| 255 |
+
position_ids=position_ids,
|
| 256 |
+
cross_attention_states=cross_attention_states,
|
| 257 |
+
cross_attention_mask=cross_attention_mask,
|
| 258 |
+
full_text_row_masked_out_mask=full_text_row_masked_out_mask,
|
| 259 |
+
past_key_values=past_key_values,
|
| 260 |
+
use_cache=use_cache,
|
| 261 |
+
inputs_embeds=inputs_embeds,
|
| 262 |
+
labels=labels,
|
| 263 |
+
output_hidden_states=output_hidden_states,
|
| 264 |
+
output_attentions=output_attentions,
|
| 265 |
+
return_dict=return_dict,
|
| 266 |
+
cache_position=cache_position,
|
| 267 |
+
num_logits_to_keep=num_logits_to_keep,
|
| 268 |
+
)
|
| 269 |
+
|
| 270 |
+
return outputs
|
| 271 |
+
|
| 272 |
+
def prepare_inputs_for_generation(
|
| 273 |
+
self,
|
| 274 |
+
input_ids=None,
|
| 275 |
+
inputs_embeds=None,
|
| 276 |
+
attention_mask=None,
|
| 277 |
+
position_ids=None,
|
| 278 |
+
audio_values: Optional[torch.FloatTensor] = None,
|
| 279 |
+
audio_token_start_idx: Optional[torch.Tensor] = None,
|
| 280 |
+
audio_token_len: Optional[torch.Tensor] = None,
|
| 281 |
+
audio_len: Optional[torch.Tensor] = None,
|
| 282 |
+
pixel_values=None,
|
| 283 |
+
aspect_ratio_ids=None,
|
| 284 |
+
aspect_ratio_mask=None,
|
| 285 |
+
cross_attention_mask=None,
|
| 286 |
+
past_key_values=None,
|
| 287 |
+
use_cache=False,
|
| 288 |
+
cache_position=None,
|
| 289 |
+
num_logits_to_keep=None,
|
| 290 |
+
**kwargs,
|
| 291 |
+
):
|
| 292 |
+
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
| 293 |
+
|
| 294 |
+
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
| 295 |
+
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
| 296 |
+
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
| 297 |
+
if past_key_values is not None:
|
| 298 |
+
if inputs_embeds is not None: # Exception 1
|
| 299 |
+
input_ids = input_ids[:, -cache_position.shape[0] :]
|
| 300 |
+
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
|
| 301 |
+
input_ids = input_ids[:, cache_position]
|
| 302 |
+
|
| 303 |
+
# TODO: we have no attention_mask so this won't work, check if we really won't need attention mask and find another way
|
| 304 |
+
if attention_mask is not None and position_ids is None:
|
| 305 |
+
# create position_ids on the fly for batch generation
|
| 306 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 307 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 308 |
+
if past_key_values:
|
| 309 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 310 |
+
|
| 311 |
+
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
|
| 312 |
+
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
| 313 |
+
|
| 314 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 315 |
+
if inputs_embeds is not None and cache_position[0] == 0:
|
| 316 |
+
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
|
| 317 |
+
else:
|
| 318 |
+
# The clone here is for the same reason as for `position_ids`.
|
| 319 |
+
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
|
| 320 |
+
|
| 321 |
+
if num_logits_to_keep is not None:
|
| 322 |
+
model_inputs["num_logits_to_keep"] = num_logits_to_keep
|
| 323 |
+
|
| 324 |
+
model_inputs.update(
|
| 325 |
+
{
|
| 326 |
+
"position_ids": position_ids,
|
| 327 |
+
"cache_position": cache_position,
|
| 328 |
+
"past_key_values": past_key_values,
|
| 329 |
+
"use_cache": use_cache,
|
| 330 |
+
"attention_mask": attention_mask,
|
| 331 |
+
"cross_attention_mask": cross_attention_mask,
|
| 332 |
+
}
|
| 333 |
+
)
|
| 334 |
+
|
| 335 |
+
prefill_start_idx = 0 if cache_position is None else cache_position[0]
|
| 336 |
+
if (
|
| 337 |
+
audio_values is not None
|
| 338 |
+
and audio_token_start_idx is not None
|
| 339 |
+
and prefill_start_idx <= torch.max(audio_token_start_idx)
|
| 340 |
+
):
|
| 341 |
+
model_inputs["audio_values"] = audio_values
|
| 342 |
+
model_inputs["audio_token_start_idx"] = (
|
| 343 |
+
audio_token_start_idx - prefill_start_idx
|
| 344 |
+
)
|
| 345 |
+
model_inputs["audio_token_len"] = audio_token_len
|
| 346 |
+
model_inputs["audio_len"] = audio_len
|
| 347 |
+
|
| 348 |
+
|
| 349 |
+
# If we're in pre-fill or cacheless decoding step, then we need pixel_values and aspect ratios
|
| 350 |
+
# to compute image hidden states, otherwise they are cached within each cross attn layer
|
| 351 |
+
if cache_position[0] == 0:
|
| 352 |
+
model_inputs["pixel_values"] = pixel_values
|
| 353 |
+
model_inputs["aspect_ratio_ids"] = aspect_ratio_ids
|
| 354 |
+
model_inputs["aspect_ratio_mask"] = aspect_ratio_mask
|
| 355 |
+
|
| 356 |
+
return model_inputs
|
| 357 |
+
|
| 358 |
+
def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder, **kwargs):
|
| 359 |
+
cross_attention_mask_prev = model_kwargs.get("cross_attention_mask", None)
|
| 360 |
+
model_kwargs = super()._update_model_kwargs_for_generation(
|
| 361 |
+
outputs=outputs,
|
| 362 |
+
model_kwargs=model_kwargs,
|
| 363 |
+
is_encoder_decoder=is_encoder_decoder,
|
| 364 |
+
**kwargs,
|
| 365 |
+
)
|
| 366 |
+
|
| 367 |
+
# add cross-attn mask for new token
|
| 368 |
+
if cross_attention_mask_prev is not None:
|
| 369 |
+
model_kwargs["cross_attention_mask"] = torch.cat(
|
| 370 |
+
[cross_attention_mask_prev, cross_attention_mask_prev[:, -1:, ...]], dim=1
|
| 371 |
+
)
|
| 372 |
+
return model_kwargs
|
| 373 |
+
|
| 374 |
+
class StackAudioFrames(nn.Module):
|
| 375 |
+
"""
|
| 376 |
+
Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`.
|
| 377 |
+
|
| 378 |
+
The number of output frames will be `ceil(T / stack_factor) + 1` where `T` is the number of input frames.
|
| 379 |
+
NOTE: the extra +1 is intentional: in case the number of audio tokens are over-estimated by the processor,
|
| 380 |
+
we want to make sure `processor.audio_token_replacement` (i.e. EOS) doesn't get leaked into the middle of embeddings.
|
| 381 |
+
In most cases this extra padding will get removed in the model's forward function so it has no effect.
|
| 382 |
+
"""
|
| 383 |
+
|
| 384 |
+
def __init__(self, stack_factor: int = 8):
|
| 385 |
+
super().__init__()
|
| 386 |
+
self.stack_factor = stack_factor
|
| 387 |
+
|
| 388 |
+
def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
|
| 389 |
+
B, T, C = audio_embeds.shape
|
| 390 |
+
T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
|
| 391 |
+
audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T + self.stack_factor))
|
| 392 |
+
B, T, C = audio_embeds.shape
|
| 393 |
+
audio_embeds = audio_embeds.view(
|
| 394 |
+
B, T // self.stack_factor, C * self.stack_factor
|
| 395 |
+
)
|
| 396 |
+
return audio_embeds
|
| 397 |
+
|
| 398 |
+
class RMSNorm(LlamaRMSNorm):
|
| 399 |
+
def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
|
| 400 |
+
super().__init__(hidden_size=hidden_size, eps=eps)
|
| 401 |
+
self.weight.data.fill_(init)
|
| 402 |
+
|
| 403 |
+
class SwiGLU(nn.Module):
|
| 404 |
+
def forward(self, x):
|
| 405 |
+
x, gate = x.chunk(2, dim=-1)
|
| 406 |
+
return F.silu(gate) * x
|
| 407 |
+
|
| 408 |
+
class UltravoxProjector(nn.Sequential):
|
| 409 |
+
def __init__(self, config: MllamaAudioConfig):
|
| 410 |
+
super().__init__()
|
| 411 |
+
self.hidden_dim = config.hidden_size
|
| 412 |
+
self._pad_and_stack = StackAudioFrames(config.stack_factor)
|
| 413 |
+
dim = config.input_hidden_size * config.stack_factor
|
| 414 |
+
self.ln_pre = RMSNorm(dim, init=config.norm_init)
|
| 415 |
+
self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
|
| 416 |
+
dim = self.hidden_dim
|
| 417 |
+
self.act = SwiGLU()
|
| 418 |
+
dim = dim // 2
|
| 419 |
+
self.linear_2 = nn.Linear(dim, config.output_hidden_size, bias=False)
|
| 420 |
+
self.ln_post = RMSNorm(config.output_hidden_size, init=config.norm_init)
|
| 421 |
+
|
| 422 |
+
def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
|
| 423 |
+
audio_features = self._pad_and_stack(audio_features)
|
| 424 |
+
audio_features = self.ln_pre(audio_features)
|
| 425 |
+
hidden_states = self.linear_1(audio_features)
|
| 426 |
+
hidden_states = self.act(hidden_states)
|
| 427 |
+
hidden_states = self.linear_2(hidden_states)
|
| 428 |
+
hidden_states = self.ln_post(hidden_states)
|
| 429 |
+
return hidden_states
|
| 430 |
+
|
| 431 |
+
|
| 432 |
+
class ModifiedWhisperEncoder(WhisperEncoder, ModuleUtilsMixin):
|
| 433 |
+
"""
|
| 434 |
+
Encoder portion of OpenAI's Whisper model.
|
| 435 |
+
This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
|
| 436 |
+
1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
|
| 437 |
+
2. allow less than 30 second of audio padding to be passed in:
|
| 438 |
+
- relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
|
| 439 |
+
- embed_pos is now sliced to match the length of `inputs_embeds`
|
| 440 |
+
Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
|
| 441 |
+
"""
|
| 442 |
+
|
| 443 |
+
base_model_prefix = "model.encoder"
|
| 444 |
+
_no_split_modules = ["WhisperEncoderLayer"]
|
| 445 |
+
|
| 446 |
+
def forward(
|
| 447 |
+
self,
|
| 448 |
+
input_features,
|
| 449 |
+
audio_len=None,
|
| 450 |
+
head_mask=None,
|
| 451 |
+
output_attentions=None,
|
| 452 |
+
output_hidden_states=None,
|
| 453 |
+
return_dict=None,
|
| 454 |
+
):
|
| 455 |
+
expected_seq_length = (
|
| 456 |
+
self.config.max_source_positions
|
| 457 |
+
* self.conv1.stride[0]
|
| 458 |
+
* self.conv2.stride[0]
|
| 459 |
+
)
|
| 460 |
+
if input_features.shape[-1] > expected_seq_length:
|
| 461 |
+
raise ValueError(
|
| 462 |
+
f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
|
| 463 |
+
)
|
| 464 |
+
|
| 465 |
+
output_attentions = (
|
| 466 |
+
output_attentions
|
| 467 |
+
if output_attentions is not None
|
| 468 |
+
else self.config.output_attentions
|
| 469 |
+
)
|
| 470 |
+
output_hidden_states = (
|
| 471 |
+
output_hidden_states
|
| 472 |
+
if output_hidden_states is not None
|
| 473 |
+
else self.config.output_hidden_states
|
| 474 |
+
)
|
| 475 |
+
return_dict = (
|
| 476 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 477 |
+
)
|
| 478 |
+
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
|
| 479 |
+
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
|
| 480 |
+
|
| 481 |
+
inputs_embeds = inputs_embeds.permute(0, 2, 1)
|
| 482 |
+
embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]
|
| 483 |
+
|
| 484 |
+
hidden_states = inputs_embeds + embed_pos
|
| 485 |
+
hidden_states = nn.functional.dropout(
|
| 486 |
+
hidden_states, p=self.dropout, training=self.training
|
| 487 |
+
)
|
| 488 |
+
|
| 489 |
+
encoder_states = () if output_hidden_states else None
|
| 490 |
+
all_attentions = () if output_attentions else None
|
| 491 |
+
|
| 492 |
+
attention_mask = None
|
| 493 |
+
if audio_len != None:
|
| 494 |
+
audio_feature_len = self._get_feat_extract_output_lengths(audio_len)
|
| 495 |
+
batch_size = hidden_states.shape[0]
|
| 496 |
+
max_seq_len = hidden_states.shape[1]
|
| 497 |
+
attention_mask = (
|
| 498 |
+
torch.arange(max_seq_len, device=hidden_states.device)[None, :]
|
| 499 |
+
.expand(batch_size, -1)
|
| 500 |
+
.lt(audio_feature_len.view(batch_size, 1))
|
| 501 |
+
)
|
| 502 |
+
attention_mask = self.get_extended_attention_mask(
|
| 503 |
+
attention_mask,
|
| 504 |
+
None,
|
| 505 |
+
device=hidden_states.device,
|
| 506 |
+
dtype=hidden_states.dtype,
|
| 507 |
+
)
|
| 508 |
+
|
| 509 |
+
# check if head_mask has a correct number of layers specified if desired
|
| 510 |
+
if head_mask is not None:
|
| 511 |
+
assert head_mask.size()[0] == (
|
| 512 |
+
len(self.layers)
|
| 513 |
+
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
|
| 514 |
+
|
| 515 |
+
for idx, encoder_layer in enumerate(self.layers):
|
| 516 |
+
if output_hidden_states:
|
| 517 |
+
encoder_states = encoder_states + (hidden_states,)
|
| 518 |
+
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
|
| 519 |
+
to_drop = False
|
| 520 |
+
if self.training:
|
| 521 |
+
dropout_probability = torch.rand([])
|
| 522 |
+
if dropout_probability < self.layerdrop: # skip the layer
|
| 523 |
+
to_drop = True
|
| 524 |
+
|
| 525 |
+
if to_drop:
|
| 526 |
+
layer_outputs = (None, None)
|
| 527 |
+
else:
|
| 528 |
+
if self.gradient_checkpointing and self.training:
|
| 529 |
+
layer_outputs = self._gradient_checkpointing_func(
|
| 530 |
+
encoder_layer.__call__,
|
| 531 |
+
hidden_states,
|
| 532 |
+
attention_mask,
|
| 533 |
+
(head_mask[idx] if head_mask is not None else None),
|
| 534 |
+
output_attentions,
|
| 535 |
+
)
|
| 536 |
+
else:
|
| 537 |
+
layer_outputs = encoder_layer(
|
| 538 |
+
hidden_states,
|
| 539 |
+
attention_mask,
|
| 540 |
+
layer_head_mask=(
|
| 541 |
+
head_mask[idx] if head_mask is not None else None
|
| 542 |
+
),
|
| 543 |
+
output_attentions=output_attentions,
|
| 544 |
+
)
|
| 545 |
+
|
| 546 |
+
hidden_states = layer_outputs[0]
|
| 547 |
+
|
| 548 |
+
if output_attentions:
|
| 549 |
+
all_attentions = all_attentions + (layer_outputs[1],)
|
| 550 |
+
|
| 551 |
+
hidden_states = self.layer_norm(hidden_states)
|
| 552 |
+
if output_hidden_states:
|
| 553 |
+
encoder_states = encoder_states + (hidden_states,)
|
| 554 |
+
|
| 555 |
+
if not return_dict:
|
| 556 |
+
return tuple(
|
| 557 |
+
v
|
| 558 |
+
for v in [hidden_states, encoder_states, all_attentions]
|
| 559 |
+
if v is not None
|
| 560 |
+
)
|
| 561 |
+
return BaseModelOutput(
|
| 562 |
+
last_hidden_state=hidden_states,
|
| 563 |
+
hidden_states=encoder_states,
|
| 564 |
+
attentions=all_attentions,
|
| 565 |
+
)
|
| 566 |
+
|
| 567 |
+
OcisMllamaConfig.register_for_auto_class()
|
| 568 |
+
OcisMllamaForConditionalGeneration.register_for_auto_class()
|
| 569 |
+
AutoConfig.register("ocismllama", OcisMllamaConfig)
|
| 570 |
+
AutoModel.register(OcisMllamaConfig, OcisMllamaForConditionalGeneration)
|
ocismllama_pipeline.py
ADDED
|
@@ -0,0 +1,134 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import logging
|
| 2 |
+
from typing import Any, Dict, List, Optional
|
| 3 |
+
|
| 4 |
+
import numpy as np
|
| 5 |
+
import transformers
|
| 6 |
+
|
| 7 |
+
# We must use relative import in this directory to allow uploading to HF Hub
|
| 8 |
+
# Even "from . import X" pattern doesn't work (undocumented and unclear why)
|
| 9 |
+
from modeling_ocismllama import OcisMllamaForConditionalGeneration
|
| 10 |
+
from ocismllama_processing import OcisMllamaProcessor
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
class OcisMllamaPipeline(transformers.Pipeline):
|
| 14 |
+
_load_processor = False
|
| 15 |
+
_load_image_processor = False
|
| 16 |
+
_load_feature_extractor = False
|
| 17 |
+
_load_tokenizer = False
|
| 18 |
+
def __init__(
|
| 19 |
+
self,
|
| 20 |
+
model: OcisMllamaForConditionalGeneration,
|
| 21 |
+
tokenizer: Optional[transformers.PreTrainedTokenizerBase] = None,
|
| 22 |
+
audio_processor: Optional[transformers.ProcessorMixin] = None,
|
| 23 |
+
image_processor: Optional[transformers.ProcessorMixin] = None,
|
| 24 |
+
**kwargs
|
| 25 |
+
):
|
| 26 |
+
if tokenizer is None:
|
| 27 |
+
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
| 28 |
+
'meta-llama/Llama-3.2-11B-Vision-Instruct'
|
| 29 |
+
)
|
| 30 |
+
|
| 31 |
+
if audio_processor is None:
|
| 32 |
+
audio_processor = transformers.AutoProcessor.from_pretrained(
|
| 33 |
+
model.config.audio_config.audio_model_id
|
| 34 |
+
)
|
| 35 |
+
|
| 36 |
+
if image_processor is None:
|
| 37 |
+
image_processor = transformers.AutoProcessor.from_pretrained(
|
| 38 |
+
'meta-llama/Llama-3.2-11B-Vision-Instruct'
|
| 39 |
+
)
|
| 40 |
+
|
| 41 |
+
super().__init__(model=model, tokenizer=tokenizer, **kwargs)
|
| 42 |
+
|
| 43 |
+
self.processor = OcisMllamaProcessor(
|
| 44 |
+
audio_processor=audio_processor,
|
| 45 |
+
image_processor=image_processor,
|
| 46 |
+
tokenizer=tokenizer,
|
| 47 |
+
stack_factor=model.config.audio_config.stack_factor,
|
| 48 |
+
)
|
| 49 |
+
|
| 50 |
+
def _sanitize_parameters(self, **kwargs):
|
| 51 |
+
generation_keys = ["temperature", "max_new_tokens", "repetition_penalty"]
|
| 52 |
+
generation_kwargs = {k: kwargs[k] for k in kwargs if k in generation_keys}
|
| 53 |
+
return {}, generation_kwargs, {}
|
| 54 |
+
|
| 55 |
+
def preprocess(self, inputs: Dict[str, Any]):
|
| 56 |
+
turns: list = inputs.get("turns", [])
|
| 57 |
+
|
| 58 |
+
audio = inputs.get("audio", None)
|
| 59 |
+
# Convert to float32 if needed.
|
| 60 |
+
if isinstance(audio, np.ndarray):
|
| 61 |
+
if audio.dtype == np.float64:
|
| 62 |
+
audio = audio.astype(np.float32)
|
| 63 |
+
elif audio.dtype == np.int16:
|
| 64 |
+
audio = audio.astype(np.float32) / np.float32(32768.0)
|
| 65 |
+
elif audio.dtype == np.int32:
|
| 66 |
+
audio = audio.astype(np.float32) / np.float32(2147483648.0)
|
| 67 |
+
|
| 68 |
+
if audio is not None and (len(turns) == 0 or turns[-1]["role"] != "user"):
|
| 69 |
+
prompt = inputs.get("prompt", "<|audio|>")
|
| 70 |
+
if "<|audio|>" not in prompt:
|
| 71 |
+
logging.warning(
|
| 72 |
+
"Prompt does not contain '<|audio|>', appending '<|audio|>' to the end of the prompt."
|
| 73 |
+
)
|
| 74 |
+
|
| 75 |
+
prompt += " <|audio|>"
|
| 76 |
+
turns.append({"role": "user", "content": prompt})
|
| 77 |
+
|
| 78 |
+
text = self.processor.tokenizer.apply_chat_template(
|
| 79 |
+
turns, add_generation_prompt=True, tokenize=False
|
| 80 |
+
)
|
| 81 |
+
|
| 82 |
+
if "sampling_rate" not in inputs and audio is not None:
|
| 83 |
+
logging.warning(
|
| 84 |
+
"No sampling rate provided, using default of 16kHz. We highly recommend providing the correct sampling rate."
|
| 85 |
+
)
|
| 86 |
+
|
| 87 |
+
images = inputs.get("images", None)
|
| 88 |
+
output = self.processor(
|
| 89 |
+
text=[text],
|
| 90 |
+
audio=audio,
|
| 91 |
+
images=images,
|
| 92 |
+
sampling_rate=inputs.get("sampling_rate", 16000),
|
| 93 |
+
)
|
| 94 |
+
if "audio_values" in output:
|
| 95 |
+
output["audio_values"] = output["audio_values"].to(self.model.dtype)
|
| 96 |
+
return output
|
| 97 |
+
|
| 98 |
+
def _forward(
|
| 99 |
+
self,
|
| 100 |
+
model_inputs: Dict[str, Any],
|
| 101 |
+
temperature: Optional[float] = None,
|
| 102 |
+
max_new_tokens: Optional[int] = None,
|
| 103 |
+
repetition_penalty: float = 1.0,
|
| 104 |
+
) -> List[int]:
|
| 105 |
+
temperature = temperature or None
|
| 106 |
+
do_sample = temperature is not None
|
| 107 |
+
|
| 108 |
+
terminators = [self.tokenizer.eos_token_id]
|
| 109 |
+
if "<|eot_id|>" in self.tokenizer.added_tokens_encoder:
|
| 110 |
+
terminators.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
|
| 111 |
+
|
| 112 |
+
input_len = model_inputs["input_ids"].shape[1]
|
| 113 |
+
model_inputs['input_ids'][model_inputs['input_ids']==128256] = 128004
|
| 114 |
+
outputs = self.model.generate(
|
| 115 |
+
**model_inputs,
|
| 116 |
+
do_sample=do_sample,
|
| 117 |
+
temperature=temperature,
|
| 118 |
+
max_new_tokens=max_new_tokens,
|
| 119 |
+
repetition_penalty=repetition_penalty,
|
| 120 |
+
eos_token_id=terminators
|
| 121 |
+
)
|
| 122 |
+
return outputs[0][input_len:]
|
| 123 |
+
|
| 124 |
+
def postprocess(self, model_outputs) -> str:
|
| 125 |
+
output_text = self.tokenizer.decode(model_outputs, skip_special_tokens=True)
|
| 126 |
+
return output_text
|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
|
| 130 |
+
"ocismllama-pipeline",
|
| 131 |
+
pipeline_class=OcisMllamaPipeline,
|
| 132 |
+
pt_model=transformers.AutoModel,
|
| 133 |
+
type="multimodal",
|
| 134 |
+
)
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|eot_id|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "<|finetune_right_pad_id|>",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
}
|
| 23 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9816d43bd5347d64bccc66b7710947fb18e9818cc660215b1462061d4a44e449
|
| 3 |
+
size 17210088
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2071 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|finetune_right_pad_id|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|step_id|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|eom_id|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|python_tag|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|reserved_special_token_2|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128012": {
|
| 100 |
+
"content": "<|reserved_special_token_3|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128013": {
|
| 108 |
+
"content": "<|reserved_special_token_4|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"128014": {
|
| 116 |
+
"content": "<|reserved_special_token_5|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": true
|
| 122 |
+
},
|
| 123 |
+
"128015": {
|
| 124 |
+
"content": "<|reserved_special_token_6|>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": true
|
| 130 |
+
},
|
| 131 |
+
"128016": {
|
| 132 |
+
"content": "<|reserved_special_token_7|>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": true
|
| 138 |
+
},
|
| 139 |
+
"128017": {
|
| 140 |
+
"content": "<|reserved_special_token_8|>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": true
|
| 146 |
+
},
|
| 147 |
+
"128018": {
|
| 148 |
+
"content": "<|reserved_special_token_9|>",
|
| 149 |
+
"lstrip": false,
|
| 150 |
+
"normalized": false,
|
| 151 |
+
"rstrip": false,
|
| 152 |
+
"single_word": false,
|
| 153 |
+
"special": true
|
| 154 |
+
},
|
| 155 |
+
"128019": {
|
| 156 |
+
"content": "<|reserved_special_token_10|>",
|
| 157 |
+
"lstrip": false,
|
| 158 |
+
"normalized": false,
|
| 159 |
+
"rstrip": false,
|
| 160 |
+
"single_word": false,
|
| 161 |
+
"special": true
|
| 162 |
+
},
|
| 163 |
+
"128020": {
|
| 164 |
+
"content": "<|reserved_special_token_11|>",
|
| 165 |
+
"lstrip": false,
|
| 166 |
+
"normalized": false,
|
| 167 |
+
"rstrip": false,
|
| 168 |
+
"single_word": false,
|
| 169 |
+
"special": true
|
| 170 |
+
},
|
| 171 |
+
"128021": {
|
| 172 |
+
"content": "<|reserved_special_token_12|>",
|
| 173 |
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| 1818 |
+
},
|
| 1819 |
+
"128227": {
|
| 1820 |
+
"content": "<|reserved_special_token_218|>",
|
| 1821 |
+
"lstrip": false,
|
| 1822 |
+
"normalized": false,
|
| 1823 |
+
"rstrip": false,
|
| 1824 |
+
"single_word": false,
|
| 1825 |
+
"special": true
|
| 1826 |
+
},
|
| 1827 |
+
"128228": {
|
| 1828 |
+
"content": "<|reserved_special_token_219|>",
|
| 1829 |
+
"lstrip": false,
|
| 1830 |
+
"normalized": false,
|
| 1831 |
+
"rstrip": false,
|
| 1832 |
+
"single_word": false,
|
| 1833 |
+
"special": true
|
| 1834 |
+
},
|
| 1835 |
+
"128229": {
|
| 1836 |
+
"content": "<|reserved_special_token_220|>",
|
| 1837 |
+
"lstrip": false,
|
| 1838 |
+
"normalized": false,
|
| 1839 |
+
"rstrip": false,
|
| 1840 |
+
"single_word": false,
|
| 1841 |
+
"special": true
|
| 1842 |
+
},
|
| 1843 |
+
"128230": {
|
| 1844 |
+
"content": "<|reserved_special_token_221|>",
|
| 1845 |
+
"lstrip": false,
|
| 1846 |
+
"normalized": false,
|
| 1847 |
+
"rstrip": false,
|
| 1848 |
+
"single_word": false,
|
| 1849 |
+
"special": true
|
| 1850 |
+
},
|
| 1851 |
+
"128231": {
|
| 1852 |
+
"content": "<|reserved_special_token_222|>",
|
| 1853 |
+
"lstrip": false,
|
| 1854 |
+
"normalized": false,
|
| 1855 |
+
"rstrip": false,
|
| 1856 |
+
"single_word": false,
|
| 1857 |
+
"special": true
|
| 1858 |
+
},
|
| 1859 |
+
"128232": {
|
| 1860 |
+
"content": "<|reserved_special_token_223|>",
|
| 1861 |
+
"lstrip": false,
|
| 1862 |
+
"normalized": false,
|
| 1863 |
+
"rstrip": false,
|
| 1864 |
+
"single_word": false,
|
| 1865 |
+
"special": true
|
| 1866 |
+
},
|
| 1867 |
+
"128233": {
|
| 1868 |
+
"content": "<|reserved_special_token_224|>",
|
| 1869 |
+
"lstrip": false,
|
| 1870 |
+
"normalized": false,
|
| 1871 |
+
"rstrip": false,
|
| 1872 |
+
"single_word": false,
|
| 1873 |
+
"special": true
|
| 1874 |
+
},
|
| 1875 |
+
"128234": {
|
| 1876 |
+
"content": "<|reserved_special_token_225|>",
|
| 1877 |
+
"lstrip": false,
|
| 1878 |
+
"normalized": false,
|
| 1879 |
+
"rstrip": false,
|
| 1880 |
+
"single_word": false,
|
| 1881 |
+
"special": true
|
| 1882 |
+
},
|
| 1883 |
+
"128235": {
|
| 1884 |
+
"content": "<|reserved_special_token_226|>",
|
| 1885 |
+
"lstrip": false,
|
| 1886 |
+
"normalized": false,
|
| 1887 |
+
"rstrip": false,
|
| 1888 |
+
"single_word": false,
|
| 1889 |
+
"special": true
|
| 1890 |
+
},
|
| 1891 |
+
"128236": {
|
| 1892 |
+
"content": "<|reserved_special_token_227|>",
|
| 1893 |
+
"lstrip": false,
|
| 1894 |
+
"normalized": false,
|
| 1895 |
+
"rstrip": false,
|
| 1896 |
+
"single_word": false,
|
| 1897 |
+
"special": true
|
| 1898 |
+
},
|
| 1899 |
+
"128237": {
|
| 1900 |
+
"content": "<|reserved_special_token_228|>",
|
| 1901 |
+
"lstrip": false,
|
| 1902 |
+
"normalized": false,
|
| 1903 |
+
"rstrip": false,
|
| 1904 |
+
"single_word": false,
|
| 1905 |
+
"special": true
|
| 1906 |
+
},
|
| 1907 |
+
"128238": {
|
| 1908 |
+
"content": "<|reserved_special_token_229|>",
|
| 1909 |
+
"lstrip": false,
|
| 1910 |
+
"normalized": false,
|
| 1911 |
+
"rstrip": false,
|
| 1912 |
+
"single_word": false,
|
| 1913 |
+
"special": true
|
| 1914 |
+
},
|
| 1915 |
+
"128239": {
|
| 1916 |
+
"content": "<|reserved_special_token_230|>",
|
| 1917 |
+
"lstrip": false,
|
| 1918 |
+
"normalized": false,
|
| 1919 |
+
"rstrip": false,
|
| 1920 |
+
"single_word": false,
|
| 1921 |
+
"special": true
|
| 1922 |
+
},
|
| 1923 |
+
"128240": {
|
| 1924 |
+
"content": "<|reserved_special_token_231|>",
|
| 1925 |
+
"lstrip": false,
|
| 1926 |
+
"normalized": false,
|
| 1927 |
+
"rstrip": false,
|
| 1928 |
+
"single_word": false,
|
| 1929 |
+
"special": true
|
| 1930 |
+
},
|
| 1931 |
+
"128241": {
|
| 1932 |
+
"content": "<|reserved_special_token_232|>",
|
| 1933 |
+
"lstrip": false,
|
| 1934 |
+
"normalized": false,
|
| 1935 |
+
"rstrip": false,
|
| 1936 |
+
"single_word": false,
|
| 1937 |
+
"special": true
|
| 1938 |
+
},
|
| 1939 |
+
"128242": {
|
| 1940 |
+
"content": "<|reserved_special_token_233|>",
|
| 1941 |
+
"lstrip": false,
|
| 1942 |
+
"normalized": false,
|
| 1943 |
+
"rstrip": false,
|
| 1944 |
+
"single_word": false,
|
| 1945 |
+
"special": true
|
| 1946 |
+
},
|
| 1947 |
+
"128243": {
|
| 1948 |
+
"content": "<|reserved_special_token_234|>",
|
| 1949 |
+
"lstrip": false,
|
| 1950 |
+
"normalized": false,
|
| 1951 |
+
"rstrip": false,
|
| 1952 |
+
"single_word": false,
|
| 1953 |
+
"special": true
|
| 1954 |
+
},
|
| 1955 |
+
"128244": {
|
| 1956 |
+
"content": "<|reserved_special_token_235|>",
|
| 1957 |
+
"lstrip": false,
|
| 1958 |
+
"normalized": false,
|
| 1959 |
+
"rstrip": false,
|
| 1960 |
+
"single_word": false,
|
| 1961 |
+
"special": true
|
| 1962 |
+
},
|
| 1963 |
+
"128245": {
|
| 1964 |
+
"content": "<|reserved_special_token_236|>",
|
| 1965 |
+
"lstrip": false,
|
| 1966 |
+
"normalized": false,
|
| 1967 |
+
"rstrip": false,
|
| 1968 |
+
"single_word": false,
|
| 1969 |
+
"special": true
|
| 1970 |
+
},
|
| 1971 |
+
"128246": {
|
| 1972 |
+
"content": "<|reserved_special_token_237|>",
|
| 1973 |
+
"lstrip": false,
|
| 1974 |
+
"normalized": false,
|
| 1975 |
+
"rstrip": false,
|
| 1976 |
+
"single_word": false,
|
| 1977 |
+
"special": true
|
| 1978 |
+
},
|
| 1979 |
+
"128247": {
|
| 1980 |
+
"content": "<|reserved_special_token_238|>",
|
| 1981 |
+
"lstrip": false,
|
| 1982 |
+
"normalized": false,
|
| 1983 |
+
"rstrip": false,
|
| 1984 |
+
"single_word": false,
|
| 1985 |
+
"special": true
|
| 1986 |
+
},
|
| 1987 |
+
"128248": {
|
| 1988 |
+
"content": "<|reserved_special_token_239|>",
|
| 1989 |
+
"lstrip": false,
|
| 1990 |
+
"normalized": false,
|
| 1991 |
+
"rstrip": false,
|
| 1992 |
+
"single_word": false,
|
| 1993 |
+
"special": true
|
| 1994 |
+
},
|
| 1995 |
+
"128249": {
|
| 1996 |
+
"content": "<|reserved_special_token_240|>",
|
| 1997 |
+
"lstrip": false,
|
| 1998 |
+
"normalized": false,
|
| 1999 |
+
"rstrip": false,
|
| 2000 |
+
"single_word": false,
|
| 2001 |
+
"special": true
|
| 2002 |
+
},
|
| 2003 |
+
"128250": {
|
| 2004 |
+
"content": "<|reserved_special_token_241|>",
|
| 2005 |
+
"lstrip": false,
|
| 2006 |
+
"normalized": false,
|
| 2007 |
+
"rstrip": false,
|
| 2008 |
+
"single_word": false,
|
| 2009 |
+
"special": true
|
| 2010 |
+
},
|
| 2011 |
+
"128251": {
|
| 2012 |
+
"content": "<|reserved_special_token_242|>",
|
| 2013 |
+
"lstrip": false,
|
| 2014 |
+
"normalized": false,
|
| 2015 |
+
"rstrip": false,
|
| 2016 |
+
"single_word": false,
|
| 2017 |
+
"special": true
|
| 2018 |
+
},
|
| 2019 |
+
"128252": {
|
| 2020 |
+
"content": "<|reserved_special_token_243|>",
|
| 2021 |
+
"lstrip": false,
|
| 2022 |
+
"normalized": false,
|
| 2023 |
+
"rstrip": false,
|
| 2024 |
+
"single_word": false,
|
| 2025 |
+
"special": true
|
| 2026 |
+
},
|
| 2027 |
+
"128253": {
|
| 2028 |
+
"content": "<|reserved_special_token_244|>",
|
| 2029 |
+
"lstrip": false,
|
| 2030 |
+
"normalized": false,
|
| 2031 |
+
"rstrip": false,
|
| 2032 |
+
"single_word": false,
|
| 2033 |
+
"special": true
|
| 2034 |
+
},
|
| 2035 |
+
"128254": {
|
| 2036 |
+
"content": "<|reserved_special_token_245|>",
|
| 2037 |
+
"lstrip": false,
|
| 2038 |
+
"normalized": false,
|
| 2039 |
+
"rstrip": false,
|
| 2040 |
+
"single_word": false,
|
| 2041 |
+
"special": true
|
| 2042 |
+
},
|
| 2043 |
+
"128255": {
|
| 2044 |
+
"content": "<|reserved_special_token_246|>",
|
| 2045 |
+
"lstrip": false,
|
| 2046 |
+
"normalized": false,
|
| 2047 |
+
"rstrip": false,
|
| 2048 |
+
"single_word": false,
|
| 2049 |
+
"special": true
|
| 2050 |
+
},
|
| 2051 |
+
"128256": {
|
| 2052 |
+
"content": "<|image|>",
|
| 2053 |
+
"lstrip": false,
|
| 2054 |
+
"normalized": false,
|
| 2055 |
+
"rstrip": false,
|
| 2056 |
+
"single_word": false,
|
| 2057 |
+
"special": true
|
| 2058 |
+
}
|
| 2059 |
+
},
|
| 2060 |
+
"bos_token": "<|begin_of_text|>",
|
| 2061 |
+
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- set user_supplied_system_message = true %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set user_supplied_system_message = false %}\n{%- endif %}\n\n{#- Find out if there are any images #}\n{% set image_ns = namespace(has_images=false) %} \n{%- for message in messages %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {%- set image_ns.has_images = true %}\n {%- endif %}\n {%- endfor %}\n{%- endfor %}\n\n{#- System message if there are no images, or if the user supplied one #}\n{%- if user_supplied_system_message or not image_ns.has_images %}\n {{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n {%- endif %}\n {{- \"Cutting Knowledge Date: December 2023\\n\" }}\n {{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot_id|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
|
| 2062 |
+
"clean_up_tokenization_spaces": true,
|
| 2063 |
+
"eos_token": "<|eot_id|>",
|
| 2064 |
+
"model_input_names": [
|
| 2065 |
+
"input_ids",
|
| 2066 |
+
"attention_mask"
|
| 2067 |
+
],
|
| 2068 |
+
"model_max_length": 131072,
|
| 2069 |
+
"pad_token": "<|finetune_right_pad_id|>",
|
| 2070 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2071 |
+
}
|