Instructions to use wheattoast11/OmniCoder-9B-Zero-Phase2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Tesslate/OmniCoder-9B")
model = PeftModel.from_pretrained(base_model, "wheattoast11/OmniCoder-9B-Zero-Phase2")

Transformers

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wheattoast11/OmniCoder-9B-Zero-Phase2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wheattoast11/OmniCoder-9B-Zero-Phase2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wheattoast11/OmniCoder-9B-Zero-Phase2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wheattoast11/OmniCoder-9B-Zero-Phase2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wheattoast11/OmniCoder-9B-Zero-Phase2

SGLang

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wheattoast11/OmniCoder-9B-Zero-Phase2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wheattoast11/OmniCoder-9B-Zero-Phase2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wheattoast11/OmniCoder-9B-Zero-Phase2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wheattoast11/OmniCoder-9B-Zero-Phase2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with Docker Model Runner:
```
docker model run hf.co/wheattoast11/OmniCoder-9B-Zero-Phase2
```

wheattoast11 commited on Apr 8

Commit

2fbc076

verified ·

1 Parent(s): 7a8e6b8

Model card: CARL Phase 1' VLM with eval results, phase transition data, Zenodo DOIs

Browse files

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README.md +69 -182

README.md CHANGED Viewed

@@ -2,210 +2,97 @@
 base_model: Tesslate/OmniCoder-9B
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:Tesslate/OmniCoder-9B
 - grpo
 - lora
-- trackio
-- trackio:https://huggingface.co/spaces/wheattoast11/trackio
-- transformers
 - trl
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## 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
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**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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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 base_model: Tesslate/OmniCoder-9B
 library_name: peft
 pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
 tags:
+- carl
+- coherence-aware-rl
 - grpo
+- vlm
+- vision-grpo
+- gui-grounding
 - lora
 - trl
+- transformers
+datasets:
+- wheattoast11/zero-rl-tool-calling-data
 ---
+# OmniCoder-9B-Zero-Phase2
+**CARL Phase 1' — VLM grounding checkpoint. EVAL: PASS (94.6% click accuracy).**
+A LoRA adapter trained with vision GRPO for GUI grounding. The model understands screenshots and produces structured coordinate output for click targets.
+## Results
+| Metric | Value |
+|--------|-------|
+| Click accuracy | **94.61%** |
+| Format compliance | **100%** |
+| Eval samples | 167 held-out |
+| Status | **PASS** |
+## Training
+- **Method:** Vision GRPO with CARL cascade rewards
+- **Base model:** [Tesslate/OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B) (Qwen3.5-9B VLM)
+- **SFT substrate:** [wheattoast11/OmniCoder-9B-Zero-Phase2-Vision-SFT](https://huggingface.co/wheattoast11/OmniCoder-9B-Zero-Phase2-Vision-SFT)
+- **Steps:** 500 GRPO steps
+- **Hardware:** 1x L40S 48GB, bf16, LoRA r=64
+- **Dataset:** [wheattoast11/grounding-with-images](https://huggingface.co/datasets/wheattoast11/grounding-with-images) (20K samples)
+### Phase Transition Observed
+During SFT, the model exhibited a first-order phase transition:
+- Steps 0-10: Baseline (3% accuracy, entropy 1.0)
+- Steps 10-20: Melting (entropy spikes to 9.3)
+- Steps 20-25: **Transition** (accuracy jumps 57 points in 5 steps)
+- Steps 25-35: Crystallization (99% accuracy, entropy 0.4)
+- Steps 35-46: Converged (99.3%, entropy 0.12)
+Consistent with Kuramoto synchronization in coupled oscillator systems.
+## Theoretical Foundation
+1. **Bounded Informational Time Crystals** — DOI: [10.5281/zenodo.18906944](https://doi.org/10.5281/zenodo.18906944)
+2. **Material Reality** — DOI: [10.5281/zenodo.18992029](https://doi.org/10.5281/zenodo.18992029)
+3. **Semantic Realizability** — DOI: [10.5281/zenodo.18992031](https://doi.org/10.5281/zenodo.18992031)
+## Usage
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+from peft import PeftModel
+base = AutoModelForImageTextToText.from_pretrained(
+    "Tesslate/OmniCoder-9B",
+    torch_dtype="bfloat16",
+    device_map="cuda:0",
+)
+model = PeftModel.from_pretrained(base, "wheattoast11/OmniCoder-9B-Zero-Phase2")
+model = model.merge_and_unload()
+processor = AutoProcessor.from_pretrained(
+    "Tesslate/OmniCoder-9B",
+    min_pixels=256*28*28,
+    max_pixels=1280*28*28,
+)
+```
+## Citation
+```bibtex
+@article{desai2026carl,
+  title   = {Coherence-Aware Reinforcement Learning},
+  author  = {Desai, Tej},
+  year    = {2026},
+  url     = {https://github.com/wheattoast11/carl},
+  note    = {Intuition Labs LLC}
+}
+```
+## License
+Apache 2.0 — Intuition Labs LLC