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 / GOAL_COMPLETION_AUDIT.md

restokes92

Polish Kaiju Coder 7 Hugging Face model cards

b040ee3 verified 5 days ago

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Kaiju Coder 7 Goal Completion Audit

Generated: 2026-06-04T02:44:04Z

Overall: complete Summary: 18 passed / 0 blocked / 0 manual

This audit maps the active Kaiju Coder 7 objective to current evidence across local runtime, Hugging Face release, OpenCode, paid API preflight, and remaining honest caveats.

Readiness Commands

Check	Ready	Return Code
Local public-testing readiness	`True`	`0`
Hugging Face release readiness	`True`	`0`
Public launch readiness	`True`	`0`
Paid API scaffold	`True`	`0`
Paid API launch	`True`	`0`
HF staging integrity	`True`	`0`
HF namespace permission evidence	`True`	`0`
Human public review	`True`	`0`

Requirement Audit

Area	Requirement	Status	Evidence
Identity	Product name is Kaiju Coder 7 and public/API model id is kaiju-coder-7.	`passed`	scripts/check_kaiju_public_release_readiness.py --mode local; release/PUBLIC_TESTING_QUICKSTART.md
OpenCode	Kaiju-specific OpenCode config installs the model, default agent, hidden artifact routing, and no-autocontinue loop guard.	`passed`	.opencode/agents/kaiju-coder-7.md; scripts/opencode-kaiju-no-autocontinue.mjs; scripts/install_kaiju_opencode_profile.py
OpenCode	After install, plain opencode/opencode run works from this Mac with Kaiju as the selected/default model.	`passed`	runs/public-opencode-smoke latest passing summary; scripts/run_kaiju_public_opencode_smoke.py
OpenCode	Customer-readiness pack passes without wrong-directory output, fake compaction completion, missing files, or secret leakage.	`passed`	runs/opencode-customer-readiness/20260603T185835Z/summary.md
Runtime	Direct API smoke passes using model=kaiju-coder-7.	`passed`	runs/benchmarks/20260603T223337Z-kaiju-coder-7-serving/summary.md
Runtime	12k, 16k, 24k, and 32k context benchmarks are recorded with a recommended default.	`passed`	release/SERVING_BENCHMARKS.md records 12288, 16384, 24576, 32768 and recommends 16k live default
Runtime	SGLang and vLLM/practical faster serving path are benchmarked honestly.	`passed`	release/SERVING_BENCHMARKS.md; release/quantized-runtime/README.md
Runtime	At least one public-friendly quantized/local candidate is working or clearly documented as blocked with evidence.	`passed`	release/quantized-runtime/README.md documents vLLM bitsandbytes runtime candidate and persisted-weights limitation
Hugging Face	Public-friendly HF release structure is staged with adapter, OpenCode helper, runtime-quantized helper, model cards, provenance, evals, and docs.	`passed`	python3 scripts/check_hf_staging_integrity.py --require-checksums
Hugging Face	At least one public Hugging Face release path is ready to upload or uploaded.	`passed`	python3 scripts/check_kaiju_public_release_readiness.py --mode hf-release
Hugging Face	Merged 51GB model repo upload is complete and public, or guarded with explicit evidence.	`passed`	release/HF_UPLOAD_EVIDENCE.md; scripts/prepare_hf_merged_model_metadata.sh; scripts/upload_hf_merged_model_from_gojira_b.sh
Hugging Face	Uploaded Hugging Face repos are downloadable by intended users.	`passed`	release/HF_UPLOAD_EVIDENCE.md; python3 scripts/check_hf_uploaded_release.py --namespace RMDWLLC --apply
Quality	Customer-style evals cover website, proposal, Stripe/payment, CRM/reporting, CSV/parser, Kiyomi operating pack, and safety/provenance.	`passed`	evals/tasks/opencode-customer-readiness.jsonl; runs/opencode-customer-readiness/20260603T185835Z/summary.md
Quality	Model/harness prompts produce file-oriented business-owner artifacts rather than vague advice.	`passed`	kaiju_harness/business_suite.py; release/EVAL_SCOREBOARD.md
Provenance	Training/eval provenance is preserved and public docs avoid internal checkpoint naming except license/provenance attribution.	`passed`	release/SOURCE_INVENTORY.md; release/DATA_PROVENANCE_DRAFT.md; release/PUBLIC_TESTING_QUICKSTART.md
Paid API	Paid API scaffold covers API keys, Stripe billing, rate limits, logging controls, abuse controls, rollback plan, and pricing assumptions.	`passed`	python3 scripts/check_paid_api_readiness.py --mode scaffold; gateway/cloudflare-worker tests
Paid API	Paid API is ready for public charging.	`passed`	python3 scripts/check_paid_api_readiness.py --mode launch
Final Report	Final report includes exact commands run, eval results, changed files, remaining risks, and what Richard should test first.	`passed`	release/FINAL_RELEASE_REPORT.md

Blocking Items

No blocking items.

Commands To Re-run

python3 scripts/check_kaiju_public_release_readiness.py --mode local
python3 scripts/check_kaiju_public_release_readiness.py --mode hf-release
python3 scripts/check_kaiju_public_release_readiness.py --mode public
python3 scripts/check_paid_api_readiness.py --mode scaffold
python3 scripts/check_paid_api_readiness.py --mode launch
python3 scripts/check_hf_staging_integrity.py --require-checksums
python3 scripts/check_hf_release_permission_evidence.py
python3 scripts/check_hf_uploaded_release.py --namespace RMDWLLC --apply
python3 scripts/check_human_release_review.py --mode public