Instructions to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0 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 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")
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("LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0")
model = AutoModelForMultimodalLM.from_pretrained("LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0", 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=40)
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 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"
# 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",
		"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

SGLang

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

Docker Model Runner
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0 with Docker Model Runner:
```
docker model run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.8-27B-Humanlike-Chat 2.0 (BF16 safetensors)

A 27B model that texts like a person and still does the work. No system prompt needed. This repo has the full BF16 weights for vLLM, SGLang and transformers.

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:

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

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 (the defaults in generation_config.json). For SGLang, point --model-path at this repo.

--language-model-only skips the vision tower. The weights keep the base's vision tower and MTP head unchanged, but 2.0 was tuned and tested on text only.

VRAM

The weights take 50.2 GiB in vLLM. Use one 80 GB card (H100, A100 80GB) or two 48 GB cards with --tensor-parallel-size 2. With 24 to 48 GB, take the FP8 repo or a GGUF 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.

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

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: -

Safetensors

Model size

28B params

Tensor type

BF16

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

Base model

Qwen/Qwen3.8-27B

Finetuned

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

Finetuned

(4)

this model

Quantizations

2 models