Instructions to use Auroraventures/cipher-sft-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Auroraventures/cipher-sft-merged with Transformers:

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

pipe = pipeline("text-generation", model="Auroraventures/cipher-sft-merged")
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("Auroraventures/cipher-sft-merged")
model = AutoModelForMultimodalLM.from_pretrained("Auroraventures/cipher-sft-merged")
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 Auroraventures/cipher-sft-merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Auroraventures/cipher-sft-merged

SGLang

How to use Auroraventures/cipher-sft-merged 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 "Auroraventures/cipher-sft-merged" \
    --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": "Auroraventures/cipher-sft-merged",
		"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 "Auroraventures/cipher-sft-merged" \
        --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": "Auroraventures/cipher-sft-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Auroraventures/cipher-sft-merged with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Auroraventures/cipher-sft-merged to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Auroraventures/cipher-sft-merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Auroraventures/cipher-sft-merged to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Auroraventures/cipher-sft-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use Auroraventures/cipher-sft-merged with Docker Model Runner:
```
docker model run hf.co/Auroraventures/cipher-sft-merged
```

Auroraventures commited on Apr 15

Commit

81098fd

verified ·

1 Parent(s): 30c9bde

Upload cipher-simpo-train.ipynb with huggingface_hub

Browse files

Files changed (1) hide show

cipher-simpo-train.ipynb +1 -1

cipher-simpo-train.ipynb CHANGED Viewed

@@ -19,7 +19,7 @@
    "metadata": {},
    "execution_count": null,
    "outputs": [],
-   "source": "# Cell 1 — Install\n!pip install -U unsloth trl datasets huggingface_hub -q\n!pip install --no-deps git+https://github.com/unslothai/unsloth.git@nightly -q\nimport os\nfrom google.colab import userdata\ntry:\n    os.environ['HF_TOKEN'] = userdata.get('HF_TOKEN')\nexcept Exception:\n    raise RuntimeError('Add HF_TOKEN to Colab Secrets (left sidebar key icon).')\nos.environ['WANDB_DISABLED'] = 'true'\nfrom huggingface_hub import login\nlogin(token=os.environ['HF_TOKEN'])\nprint('Setup done')"
   },
   {
    "cell_type": "code",

    "metadata": {},
    "execution_count": null,
    "outputs": [],
+   "source": "# Cell 1 — Install\n!pip install -U unsloth trl datasets huggingface_hub -q\n!pip install --no-deps git+https://github.com/unslothai/unsloth.git@nightly -q\nimport os\nfrom google.colab import userdata\n\n# Try multiple secret names (case-insensitive fallback)\nhf_token = None\nfor name in ['HF_TOKEN', 'hf_token', 'HF_TOKE2', 'HF_TOKE', 'huggingface_token']:\n    try:\n        v = userdata.get(name)\n        if v:\n            hf_token = v\n            print(f'Using token from secret: {name}')\n            break\n    except Exception:\n        continue\n\nif not hf_token:\n    raise RuntimeError(\n        'No HF token found. Add a Colab Secret named HF_TOKEN '\n        '(left sidebar 🔑 icon) with toggle ON for this notebook.'\n    )\n\nos.environ['HF_TOKEN'] = hf_token\nos.environ['WANDB_DISABLED'] = 'true'\nfrom huggingface_hub import login\nlogin(token=os.environ['HF_TOKEN'])\nprint('Setup done')"
   },
   {
    "cell_type": "code",