Instructions to use Blackfrost-AI/Qwythos-9B-EMERGED-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-AI/Qwythos-9B-EMERGED-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="Blackfrost-AI/Qwythos-9B-EMERGED-BF16")
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("Blackfrost-AI/Qwythos-9B-EMERGED-BF16")
model = AutoModelForMultimodalLM.from_pretrained("Blackfrost-AI/Qwythos-9B-EMERGED-BF16")
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 Blackfrost-AI/Qwythos-9B-EMERGED-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/Qwythos-9B-EMERGED-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackfrost-AI/Qwythos-9B-EMERGED-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-AI/Qwythos-9B-EMERGED-BF16

SGLang

How to use Blackfrost-AI/Qwythos-9B-EMERGED-BF16 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 "Blackfrost-AI/Qwythos-9B-EMERGED-BF16" \
    --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": "Blackfrost-AI/Qwythos-9B-EMERGED-BF16",
		"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 "Blackfrost-AI/Qwythos-9B-EMERGED-BF16" \
        --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": "Blackfrost-AI/Qwythos-9B-EMERGED-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Blackfrost-AI/Qwythos-9B-EMERGED-BF16 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/Qwythos-9B-EMERGED-BF16
```

Qwythos-9B-EMERGED-BF16

A reasoning + agentic distillation of the Qwythos 9B base, trained by Blackfrost AI on The Void. Full 16-bit (BF16) weights. This is the pure-reasoning build — trained on The Void with no marketing or domain data — so the base model's reasoning and tool-use are sharpened without pulling it toward any vertical. It is a vision-language model (multimodal vision inherited from the base — see Vision).

Built on empero-ai/Qwythos-9B-Claude-Mythos-5-1M. Full credit to empero-ai for the base model; Blackfrost contributes The Void distillation.

Overview


Base	`empero-ai/Qwythos-9B-Claude-Mythos-5-1M` — dense `qwen3_5` (Qwen3.6-class) 9B, ~9.5B params
Modality	Vision-language — text + image + video in, text out (vision tower inherited from base; see Vision)
Method	SFT via BF16 LoRA (Unsloth) — rank 32 / α 32, 3 epochs, length-grouped batching, loss masked to assistant turns; adapter merged back to 16-bit
Corpus	The Void v4 — ~5,032 multi-turn distilled reasoning + agentic (ReAct-style tool-use) trajectories. No marketing / domain data.
Hardware	8× NVIDIA B200 (DDP)
Precision	BF16 (`safetensors`)
Context	1,048,576 (1M) native, inherited from the base
Architecture	`Qwen3_5ForConditionalGeneration` (`qwen3_5`), dense, with vision tower

The Void

The Void is Blackfrost's proprietary distillation corpus and method: chain-of-thought reasoning and agentic tool-use trajectories harvested from a panel of frontier teacher models, curated into a clean, domain-neutral training set. Training a base model on The Void concentrates its reasoning and agentic behavior without adding vertical bias. Qwythos-9B-EMERGED is the Void-distilled build of the Qwythos base — the reasoning core, sharpened.

Vision (multimodal)

Qwythos-9B-EMERGED is a vision-language model. The base architecture (Qwen3_5ForConditionalGeneration) carries a full vision tower, and it is present in these weights (~333 vision tensors under model.visual.*) with a Qwen2VLImageProcessor (patch size 16) plus image and video tokens — so the model accepts images and video alongside text.

Scope of Blackfrost's work: The Void v4 distillation was text-only (reasoning + agentic trajectories; LoRA on the language tower). The vision tower is inherited unchanged from the base and was not part of Blackfrost's training or evaluation — multimodal behavior tracks the base model, so validate it for your use case.

The A/B design

EMERGED is one half of a matched pair — both trained from the same base on the same reasoning foundation, differing only in whether a domain corpus was layered on top:

Build	Trained on	For
`Qwythos-9B-EMERGED-BF16` (this repo)	The Void v4 only	pure reasoning / agentic — no vertical bias
`BF-BIS-SERIES-MARKETING-9B`	The Void v4 + a marketing/business corpus	brand voice, ad copy, marketing strategy

The pair isolates the effect of layering a domain corpus on top of the same reasoning foundation.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Blackfrost-AI/Qwythos-9B-EMERGED-BF16"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "Explain why a hash table has O(1) average lookup."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Generate with thinking enabled to surface the distilled reasoning trace. For image/video input, load with the multimodal processor (AutoProcessor / AutoModelForImageTextToText) and use the packaged chat template.

Status & limitations

Generation-verified by Blackfrost on text (loads and produces coherent reasoning); not yet published with a full public benchmark suite. Vision is inherited from the base and not independently evaluated.
A 9B model — verify important facts. It inherits the characteristics and limitations of the base model.
License is Apache-2.0, inherited from the base.

Trained and released by Blackfrost AI on The Void. Base model by empero-ai (Apache-2.0).