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Update model card with v10 eval results (92% task, 99% format)

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  ---
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- base_model: Tesslate/OmniCoder-9B
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  library_name: peft
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- pipeline_tag: text-generation
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  tags:
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- - base_model:adapter:Tesslate/OmniCoder-9B
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- - grpo
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- - lora
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- - trackio
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- - trackio:https://huggingface.co/spaces/wheattoast11/trackio
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- - transformers
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- - trl
 
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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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- ### Results
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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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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
 
 
 
 
 
 
 
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
 
 
 
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- ### Framework versions
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- - PEFT 0.18.1
 
 
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  ---
 
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  library_name: peft
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+ base_model: Tesslate/OmniCoder-9B
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  tags:
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+ - carl
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+ - terminals
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+ - intuition-labs
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+ - rl
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+ - grpo
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+ - tool-calling
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+ - coding-agent
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+ license: other
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+ # OmniCoder-9B-Zero-Phase2Prime (v10)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ **CARL** (Coherence-Aware Reinforcement Learning) LoRA adapter by [Intuition Labs](https://terminals.tech) / [Tej Desai](https://github.com/wheattoast11).
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+ Phase 2' Environment GRPO: Tool-calling through real interaction. The model learned WHICH tools solve WHICH tasks through 80 steps of GRPO with real subprocess execution.
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+ ## Eval Results (2026-04-09)
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+ | Metric | Value |
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+ |--------|-------|
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+ | Task completion | **92%** |
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+ | Tool format compliance | 99% |
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+ | Mean tool calls | 11.09 |
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+ | Individual tool failure rate | 43% (recovers via retry) |
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+ | Mean tokens | 1441 |
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+ | Phase 2' Gate | **PASS** |
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+ ## Training
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+ - **Base model:** [Tesslate/OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B)
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+ - **Method:** GRPO with CodingSandboxEnv (real subprocess execution)
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+ - **Steps:** 80 | **Generations:** 2 per prompt
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+ - **Rewards:** 5-function cascade (tool_engagement + task_completion + gated_CARL + tool_format + GR3_length)
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+ - **LoRA:** r=64, alpha=128, targets=qkvo+gate+up+down
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+ ## Usage
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+ ## CARL Naming
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+ This adapter is also available as a merged model at `wheattoast11/il-terminals-carl-omni9b-v10` (pending).
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+ Pattern: `il-terminals-carl-{base}-{tag}` | [Intuition Labs](https://terminals.tech)
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+ ## Papers
 
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+ - Bounded Informational Time Crystals (DOI: 10.5281/zenodo.18906944)
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+ - Semantic Realizability (DOI: 10.5281/zenodo.18992031)