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---
license: mit
datasets:
- JailbreakBench/JBB-Behaviors
- darkknight25/blue_team_defense_dataset
- >-
AmanPriyanshu/tool-reasoning-sft-RESEARCH-openresearcher-dataset-sft-deep-research-agent-data-cleaned
- SEBK4C/ornith-35b-agentic-eval
- agentica-org/DeepCoder-Preview-Dataset
- yzhuang/Agentic-Long-Context-Understanding-QA
- ethanker/agentic_coding_dataset
- FlameF0X/agentic-code
- SultanR/arxiv-to-code-agentic-tool-calling
- yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M
- kryp1234/Creative-Professionals-Agentic-Tasks-1M
- Petrouil/opencode-agentic-mini
- nagrajn/synthetic_linux_commands
- darkknight25/KALI_LINUX_TOOLKIT_DATASET
- barisKacin/KALI_LINUX_TOOLKIT_DATASET
- darkknight25/Linux_Terminal_Commands_Dataset
- UI-Simulator/UI-Simulator_android_data
- jtatman/python-code-dataset-500k
- tickleliu/all-skills-from-skills-sh
- LittleDinoC/agent-skills
- deciqAI/knowledge-skills
- nvidia/Nemotron-SFT-Competitive-Programming-v2
- >-
JasonYan777/PersonaSignal-PersonalizedResponse-Programming-Expertise-gpt-5-mini
- mtimur/distill-gpt4-eng-chat
- NickyNicky/nano_finance_200k_en_es_chatML_gemma_orpo_dpo
- will4381/job-posting-classification
- nonameee12233/job-resume-matching
- chayandatta/sherlock-holmes-corpus
- fluid-concepts/friend-bench
- matzejo/godot-lora-dataset
- ahmedhus22/python-static-analysis-linting
- deboradum/conspiracyTheories
- rescommons/agent-orchestration-dataset
- V1rtucious/agent-orchestration-dataset
- automationkasey/simp-guardian-dataset
- darkknight25/Networking_Commands_Dataset
- BytArch/ub-networking-dataset-2024-2
- 3amthoughts/vibe-coding-planning-dataset
- xTRam1/plan-and-act-data
- ThinkNet/HQ-Chat-1K
- zhiyuan218/Think-Bench
- opus-research/opus-thinking-10k
- Stormtrooperaim/Ultra-Thinker-30k
- nbtpj/multi-context-long-answer-dataset
- JunichiroMorita/structeval-structured-clean-thinkless
- Roman1111111/gemini-3.1-pro-hard-high-reasoning
- yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K
- AtlasUnified/Atlas-Reasoning
- ZennyKenny/synthetic_vc_financial_decisions_reasoning_dataset
- mattwesney/ToT_Reasoning_Problem_Solving_Dataset_V2
- yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M
- JiaMao/pathgen
- wordlevel/toefl-essential-vocabulary-1k
- kshitijthakkar/mcp-server-bench-gradio-optimized-full-bench
- yashsoni78/conversation_data_mcp_100
- raghavprabhakar/commonsense-embodied-ai
- rishini/adaption-marketing-optimized-neural-titans
- juiceb0xc0de/vibethinker-1.5b-atlas
- armand0e/claude-fable-5-claude-code
- Sayyam6565/claude-fable-5-claude-code
- Moeblack/artist-style-benchmark
- masterpieceexternal/gpt-oss-20b-moe-expert-power-traces-320k-ds16k
language:
- en
metrics:
- code_eval
- exact_match
- accuracy
- Camus-rebel/bertscore-with-torch_dtype
- bertscore
base_model:
- zai-org/GLM-5.2
- HuggingFaceH4/zephyr-7b-beta
- ResembleAI/chatterbox-turbo-ONNX
- agentica-org/DeepCoder-14B-Preview
new_version: dak0ta13/TitanXS
library_name: peft
tags:
- agent
- code
- text-generation-inference
- legal
- Godot
- ToolUse
- Coding
- art
- music
- moe
---
# Model Card for Model ID
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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### Recommendations
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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.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
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