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README.md
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---
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library_name: peft
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base_model: cerebras/Cerebras-GPT-256M
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license: apache-2.0
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language:
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- en
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tags:
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- pcgml
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- pcg
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- biome
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- terrain
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---
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# Model Card for Model ID
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Part of bachelor thesis focusing on generating 3d terrain from text.<br>
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Biome Definition is a json formatted string detailing the biomes and their features. <br>
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Together with SD Heightmap Generation v3.0, 3d terrain can be procedurally generated.
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<!-- Provide a quick summary of what the model is/does. -->
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## Uses
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Example Inference:<br>
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<prompt> Alpine mountains and a forest adorned with red flowers.
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<prompt> Alpine mountains and a forest adorned with red flowers.<br>
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<biomes> [{'Entities': {'Boulders': [], 'BouldersSpawnrate': 0.0, 'Grass': [{'BaseTint': '#ff0000', 'Id': 'flowers'}], 'GrassSpawnrate': 0.8, 'Trees': [], 'TreesSpawnrate': 0.0}, 'Name':'Alpine Mountains', 'SpawnCondition': {'HeightRange': [0.0, 0.5], 'SlopeRange': [0.0, 0.3]}, 'Texturing': {'GroundTexture': 'grass', 'GroundTextureTint': '#7cfc00', 'SlopeTexture': 'grass', 'SlopeTextureTint': '#7cfc00'}}, {'Entities': {'Boulders': [], 'BouldersSpawnrate': 0.0, 'Grass': [], 'GrassSpawnrate': 0.0, 'Trees': [{'BaseTint': '#8b4513', 'Id': 'tree_large'}], 'TreesSpawnrate': 0.8}, 'Name':'Redwood Forest', 'SpawnCondition': {'HeightRange': [0.0, 0.5], 'SlopeRange': [0.0, 0.3]}, 'Texturing': {'GroundTexture': 'dirt', 'GroundTextureTint': '#7cfc00', 'SlopeTexture':'stone', 'SlopeTextureTint': '#c2c2c2'}}]
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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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Training Loss: 4%
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Validation Loss: 6%
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* trees: tree_small, tree_medium, tree_large, tree_conifer_small, ...
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* grass: grass_small, grass_medium, grass_large, grass_dry, flowers
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Model also has trouble overadjusting tints, leading to oversaturated colorations.
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Model sometimes continues output. Just end prompt at "}}]".
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Model sometimes creates incomplete biome definitions or places entities on wrong biome.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Finetuned on custom dataset of size 538 json formatted biome definitions consisting of:
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* Name
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* SpawnCondition
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* HeightRange
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* SlopeRange
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* Entities
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* Trees
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* Id
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* BaseTint
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* Boulders
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* Id
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* BaseTint
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* Grass
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* Id
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* BaseTint
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* TreesSpawnrate
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* BouldersSpawnrate
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* GrassSpawnrate
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* Texturing
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* GroundTexture
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* GroundTextureTint
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* SlopeTexture
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* SlopeTextureTint
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###
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<!--
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- **Model type:** [PEFT Adapter]
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- **Language(s) (NLP):** [English]
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- **License:** [Apache 2.0]
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### Framework versions
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- PEFT 0.11.0
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---
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library_name: peft
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base_model: cerebras/Cerebras-GPT-256M
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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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- **Developed by:** [More Information Needed]
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- **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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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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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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## Uses
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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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[More Information Needed]
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### Downstream Use [optional]
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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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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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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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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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### 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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### Framework versions
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- PEFT 0.11.0
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"c_fc",
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"c_attn"
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],
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"c_proc",
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"c_fc",
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"c_attn"
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],
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:46ffde70dbe022c8b52002bfa9ed84c751eeb3903441f4ab8159f76f8239cb5e
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size 8780752
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