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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("RMDWLLC/kaiju-coder-7")
model = AutoModelForMultimodalLM.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
```

restokes92 commited on 6 days ago

Commit

736af44

verified ·

1 Parent(s): 8d8d6ea

Refresh HF upload evidence snapshot

Browse files

Files changed (1) hide show

HF_UPLOAD_EVIDENCE.md +7 -3

HF_UPLOAD_EVIDENCE.md CHANGED Viewed

@@ -6,10 +6,14 @@ Generated: `2026-06-03T20:36:26Z`
 | Repo | Visibility | Evidence |
 |---|---|---|
-| `RMDWLLC/kaiju-coder-7-adapter` | public | Final visible SHA `b9ca731973439ecc33a6d4ffd1cf7a289376d864`; public visibility verified. |
 | `RMDWLLC/kaiju-coder-7-opencode` | public | Final visible SHA `3c9c75416ffb41645a1a959beb99baeff6972fb8`; public visibility and OpenCode installer dry-run verified. |
 | `RMDWLLC/kaiju-coder-7-quantized-runtime` | public | Uploaded at commit `6d7449a3ffac68ed1d591c57b044ba599cee8b11`; public visibility verified. |
-| `RMDWLLC/kaiju-coder-7` | public | `hf upload-large-folder` completed successfully, then metadata/evidence refreshed at final visible SHA `59bbdff47f44d6a82f6a354d1c5dac7eb191486c`; public metadata reports `private: false`. |
 ## Merged Model Upload
@@ -74,7 +78,7 @@ Result:
 - The downloaded OpenCode helper installer dry-run passed and included the
   loop guard.
 - Merged model metadata reports `private: false`, SHA
-  `59bbdff47f44d6a82f6a354d1c5dac7eb191486c`, and lists all `14`
   safetensors shards.
 The earlier private-storage limit blocked private file downloads after the

 | Repo | Visibility | Evidence |
 |---|---|---|
+| `RMDWLLC/kaiju-coder-7-adapter` | public | Final visible SHA `9f36ce33fe1f38bab44719bf2dfe19f274e32928`; public visibility verified after 2026-06-03 paid API evidence refresh. |
 | `RMDWLLC/kaiju-coder-7-opencode` | public | Final visible SHA `3c9c75416ffb41645a1a959beb99baeff6972fb8`; public visibility and OpenCode installer dry-run verified. |
 | `RMDWLLC/kaiju-coder-7-quantized-runtime` | public | Uploaded at commit `6d7449a3ffac68ed1d591c57b044ba599cee8b11`; public visibility verified. |
+| `RMDWLLC/kaiju-coder-7` | public | `hf upload-large-folder` completed successfully, then metadata/evidence refreshed at final visible SHA `8d8d6ea21d14cea9af743e8f80148dc9437fe7d5`; public metadata reports `private: false`. |
+These SHAs are a point-in-time release evidence snapshot. Uploading this
+evidence file itself creates another metadata commit, so use `hf models info`
+for the current repository SHA when exact current SHA matters.
 ## Merged Model Upload
 - The downloaded OpenCode helper installer dry-run passed and included the
   loop guard.
 - Merged model metadata reports `private: false`, SHA
+  `8d8d6ea21d14cea9af743e8f80148dc9437fe7d5`, and lists all `14`
   safetensors shards.
 The earlier private-storage limit blocked private file downloads after the