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 / SOURCE_INVENTORY.md

restokes92

Add files using upload-large-folder tool

9afd28d verified 5 days ago

preview code

raw

history blame contribute delete

5.59 kB

Kaiju Source Inventory

Generated from GitHub source-of-truth repositories plus the requested local RMDW wiki snapshot. This inventory defines what may become Kaiju training data, what is eval-only, and what must stay excluded.

Global Training Rules

Do not train on raw secrets, API keys, OAuth tokens, cookies, private keys, or credential files.
Do not train on closed-model responses from OpenAI, Anthropic, Gemini, or similar providers unless the terms clearly allow it.
Do not train on client-specific private data without explicit review and consent.
Preserve repository name, commit SHA, source path, license, and reviewer status for every promoted dataset row.

GitHub Repository Inventory

Repo	SHA	Role	Training use	Required gates	Exclusions	Notes
RichardEchols/kaiju-coder	`3d57eae92ad5`	model lab, harness, evals, training scripts	candidate-after-review	secret-scan, closed-model-output-check, license-review	runs, models, .secrets, private datasets, raw logs	Use repo-owned harnesses, evals, docs, scripts, and curated datasets. Exclude weights, generated runs, and local secrets.
RichardEchols/Kiyomi-7.7.7	`294b31008135`	business-owner AI-company module contracts	candidate-after-review	secret-scan, closed-model-output-check, private-data-review	credentials, tokens, private client state, closed-model transcripts	Use module contracts, templates, acceptance gates, and owner-facing task structure as high-signal business-owner curriculum.
RichardEchols/kiyomi-agent	`b192c910f3f7`	business OS wrapper and local-agent patterns	candidate-after-review	secret-scan, closed-model-output-check, private-data-review	credentials, tokens, local runtime state, private support logs	Use architecture, docs, scripts, and safe wrapper patterns. Do not train on runtime secrets or private logs.
RichardEchols/rmdw-site	`df089dc3b2d3`	public RMDW offer, site, and conversion surface	candidate-after-review	secret-scan, closed-model-output-check, public-copy-review	environment files, deployment secrets, analytics tokens	Use public offer copy, app structure, pricing/CTA patterns, and website implementation patterns.
RichardEchols/makotoair	`7568f07fea6e`	client website implementation pattern	eval-and-patterns-only	secret-scan, client-data-review, consent-review	client-specific, contact data, contracts, private business details	Use as eval/pattern inspiration for local service business sites. Do not bulk-train on client-specific text without explicit review.
RichardEchols/Mezzal-Construction	`e8f2eede0405`	client website implementation pattern	eval-and-patterns-only	secret-scan, client-data-review, consent-review	client-specific, contact data, contracts, private business details	Use as eval/pattern inspiration for premium contractor site work. Do not bulk-train on client-specific text without explicit review.
RichardEchols/rmdw-agent-wiki	`ae1b8e85d3fe`	RMDW/Kiyomi operational wiki	selective-reference-only	secret-scan, credentials-redaction, private-data-review, closed-model-output-check	credentials.md, customers.md, raw, contracts, private client notes, support logs	Use only redacted strategy/product notes and documented decisions. Never use raw credentials or private client data.

Local Source Inventory

Local files are context snapshots, not the source of truth. Promote local wiki material into training only after explicit review, redaction, and either sync/diff against the GitHub wiki or a documented reviewer exception.

Source	Path	Git repo	Files	Training use	Required gates	Excluded paths present	Safe reference candidates	Notes
RMDW-Wiki-local	`/Users/richardecholsai7/Documents/RMDW-Wiki`	no	93	selective-reference-only	secret-scan, credentials-redaction, private-data-review, sync-or-diff-against-github	credentials.md, customers.md, customers/, raw/	README.md, kaiju-coder-build-log.md, kaiju-coder-business-plan.md, kaiju-coder-soul.md, kiyomi-agent-build-log.md, pricing-history.md, product/kiyomi-private-ai-workstation.md, ops/product-ops-automation.md, client-acquisition-engine/README.md	Use as a local context snapshot only after explicit row-level review. Do not treat unsynced local files as the authoritative training source.

Training Eligibility Meaning

candidate-after-review: source can produce training or eval examples only after secret scanning, closed-model-output review, and row-level provenance.
eval-and-patterns-only: use for hard eval prompts, harness behavior, screenshots, or generalized patterns. Do not bulk-train on client-specific source text.
selective-reference-only: use narrowly after redaction. Treat credentials, customer notes, and raw operational data as excluded by default.
Local snapshots require review against the GitHub source of truth before promotion into dataset rows.

Next Dataset Step

Generate candidate examples only from reviewed paths, attach this inventory SHA or local snapshot data to each row, then run scripts/validate_training_data.py before any training run.