Instructions to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8")
model = AutoModelForMultimodalLM.from_pretrained("LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8

SGLang

How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8 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 "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8" \
    --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": "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8",
		"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 "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8" \
        --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": "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8 with Docker Model Runner:
```
docker model run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8
```

Qwen3.8-27B-Humanlike-Chat 2.0 (FP8)

A 27B model that texts like a person and still does the work, in FP8 for 32 to 48 GB cards. No system prompt needed.

Results, examples and how it was made are on the main card: Qwen3.8-27B-Humanlike-Chat-GGUF.

"Base" here always means huihui-ai/Huihui-Qwen3.8-27B-abliterated, an abliterated Qwen3.8-27B. It is not the official Qwen release.

vLLM

Tested with vLLM 0.27.1 on an H100:

vllm serve LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8 --served-model-name humanlike-2.0 \
  --max-model-len 16384 --language-model-only --linear-backend cutlass \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3

On the H100, --linear-backend cutlass uses vLLM's precompiled block-FP8 kernel and the server is up in about 3 minutes. vLLM 0.27.1's default kernel on that card builds code at startup and needs nvcc in the container. On other GPUs, drop --linear-backend cutlass and vLLM picks a kernel for your card. Thinking is on by default. Send chat_template_kwargs: {"enable_thinking": false} per request for the fastest replies. Sampling: temperature 1.0, top_p 0.95, top_k 20.

Format

Same layout as Qwen's official Qwen/Qwen3.8-27B-FP8: FP8 e4m3 weights in 128 x 128 blocks with BF16 scales, dynamic activation scaling. 407 linear layers are FP8: MLP, attention, the linear-attention projections and the MTP head's layers. These stay BF16: embeddings, lm_head, norms, the linear-attention gate tensors (in_proj_a, in_proj_b, A_log, dt_bias, conv) and the vision tower. Made from the BF16 weights with no calibration data.

VRAM

The weights take 27.6 GiB in vLLM. A 48 GB card fits long contexts. A 32 GB card fits shorter contexts; lower --max-model-len and raise --gpu-memory-utilization to 0.95 if startup reports no memory for the cache. For 24 GB, use IQ4_XS or Q4_K_M from the main card.

Checks

Same check as every file on the main card: 5 fresh test chats against the reference (the base plus the 2.0 adapter at runtime in BF16), served on vLLM 0.27.1, one H100 NVL.

File	KL vs reference	Top-1 agreement	Greedy turns identical	Check fails (greedy / sampled)	Decode tok/s
BF16	0.0032	97.2%	12 of 15	0 / 0	46
FP8	0.0079	91.7%	9 of 15	0 / 0	72

Live requests on the same server: "yo you up" with thinking off came back yeah what's up with no reasoning. With thinking on, the train question came back 17:35 with the reasoning in its own field. A weather request produced get_weather with {"city": "Lisbon", "unit": "celsius"}.

Commissions

Commissions open, DM codebottle on Discord (https://discord.com/users/320486798859960322). I build custom finetunes like this one: characters, product voices, distillation into smaller models, and domain or use-case specific models.

License

Apache-2.0, inherited from the upstream Qwen and Huihui releases.

Downloads last month: 91

Safetensors

Model size

28B params

Tensor type

BF16

F8_E4M3

Model tree for LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0-FP8

Base model

Qwen/Qwen3.8-27B

Finetuned

huihui-ai/Huihui-Qwen3.8-27B-abliterated

Finetuned

LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0

Quantized

(5)

this model