Instructions to use RMDWLLC/kaiju-coder-7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RMDWLLC/kaiju-coder-7 with Transformers:

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

pipe = pipeline("text-generation", model="RMDWLLC/kaiju-coder-7")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("RMDWLLC/kaiju-coder-7")
model = AutoModelForImageTextToText.from_pretrained("RMDWLLC/kaiju-coder-7")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RMDWLLC/kaiju-coder-7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RMDWLLC/kaiju-coder-7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RMDWLLC/kaiju-coder-7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RMDWLLC/kaiju-coder-7

SGLang

How to use RMDWLLC/kaiju-coder-7 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 "RMDWLLC/kaiju-coder-7" \
    --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": "RMDWLLC/kaiju-coder-7",
		"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 "RMDWLLC/kaiju-coder-7" \
        --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": "RMDWLLC/kaiju-coder-7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RMDWLLC/kaiju-coder-7 with Docker Model Runner:
```
docker model run hf.co/RMDWLLC/kaiju-coder-7
```

kaiju-coder-7 / DATA_PROVENANCE_DRAFT.md

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Kaiju Coder 7 by Kiyomi - Data Provenance Draft

This draft records the current data boundary for release review.

Policy

Kaiju Coder training data must be legally usable for a commercial derivative model.

Allowed:

RMDW-authored examples.
RMDW-owned repository diffs and documentation.
Human-reviewed examples created specifically for Kaiju.
Public permissive data only when license review confirms compatibility.

Not allowed:

Closed-model answers from OpenAI, Anthropic, Gemini, or similar services as supervised completions.
Unreviewed customer data.
Private customer code without consent.
Secrets, tokens, credentials, cookies, or private keys.
Unlicensed scraped code.

v0.1 Dataset Snapshot

Total reviewed examples: 575
Dataset build: datasets/build/kaiju-sft-v0.1.jsonl
Candidate sources:
- datasets/candidates/rmdw-git-patches.jsonl
- datasets/candidates/v0.1-safe-git-backlog.jsonl
- datasets/candidates/v0.1-file-level-git.jsonl
- datasets/candidates/v0.1-wiki-strategy-business-identity.jsonl

v1.7 Business-Owner Suite Addendum

Date prepared: 2026-06-03
Reviewed examples: 8
Candidate file: datasets/candidates/v1.7-rmdw-business-owner-suite.jsonl
Addendum-only SFT build: datasets/build/kaiju-sft-v1.7-business-owner-suite.jsonl
Training SFT build: datasets/build/kaiju-sft-v1.7-business-owner-oversampled.jsonl
Training config: training/configs/qwen36-27b-lora-v1.7.example.json
v1.8 training config: training/configs/qwen36-27b-lora-v1.8-business-owner.example.json
New task type: business_suite
Source inventory: release/SOURCE_INVENTORY.md, refreshed from GitHub source-of-truth repositories and the requested local RMDW wiki snapshot.

This addendum targets Kiyomi 7.7.7 style business-owner work: complete AI-company build packs, premium service websites, intake and CRM flows, sales follow-up, proposals, ROI dashboards, operator handbooks, and Workshop golden-run automations.

Every row includes:

source_repos
source_paths
provenance_notes
reviewed: true
license: RMDW-owned

For the v1.7 LoRA run, the 8 reviewed business-owner rows are oversampled 24 times by scripts/build_v17_business_owner_sft_dataset.py. Repeated rows receive unique IDs ending in __v17_business_repeat_NN and preserve the original source repository, source path, and provenance metadata.

Client-site repositories are used only as eval and generalized pattern sources unless a row is explicitly reviewed for training eligibility. Do not bulk-train on client-specific text, contact details, contracts, or private business data.

The local wiki path /Users/richardecholsai7/Documents/RMDW-Wiki is present but is not a git checkout. It is recorded as RMDW-Wiki-local, selective-reference-only, with credentials.md, customers.md, customers/, and raw/ excluded. The GitHub RichardEchols/rmdw-agent-wiki repo remains the authoritative wiki source for training/eval provenance unless a reviewer documents a local exception.

Category Mix

The v0.1 category gate passed:

Website/UI: at least 75 examples
Coding: at least 75 examples
Debugging: at least 50 examples
Automation: at least 50 examples
Tool-use: at least 50 examples
Strategy: at least 25 examples
Business: at least 15 examples
Identity: at least 10 examples

Release Review Checklist

Before public release:

Re-run dataset validation.
Re-run source inventory against the current GitHub source-of-truth SHAs.
Spot-check examples for secrets and private data.
Confirm client-site rows are generalized pattern examples or eval-only.
Confirm closed-model outputs are not used as supervised completions.
Record exact base model revision.
Attach upstream license and notices.
Attach eval summary.
Document known limitations and unsafe use boundaries.