Instructions to use OnlyTextLLMs/Qwen3.8-27B-OnlyText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OnlyTextLLMs/Qwen3.8-27B-OnlyText with Transformers:

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

pipe = pipeline("text-generation", model="OnlyTextLLMs/Qwen3.8-27B-OnlyText")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OnlyTextLLMs/Qwen3.8-27B-OnlyText")
model = AutoModelForCausalLM.from_pretrained("OnlyTextLLMs/Qwen3.8-27B-OnlyText", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OnlyTextLLMs/Qwen3.8-27B-OnlyText with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OnlyTextLLMs/Qwen3.8-27B-OnlyText

SGLang

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

Docker Model Runner
How to use OnlyTextLLMs/Qwen3.8-27B-OnlyText with Docker Model Runner:
```
docker model run hf.co/OnlyTextLLMs/Qwen3.8-27B-OnlyText
```

Qwen3.8-27B-OnlyText / README.md

mrs83

card: refresh to the current generator (drop the hardcoded language list, name both media stacks)

195ff6e verified 15 days ago

preview code

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.8-27B
library_name: transformers
tags:
  - text-generation
  - conversational
  - text-only
pipeline_tag: text-generation

Qwen3.8-27B-OnlyText

Text-only causal language model derived from Qwen/Qwen3.8-27B by removing the vision and audio stacks (their towers/embedders and projector weights) and the multimodal special tokens. The text backbone and LM head are preserved, and the MTP draft head is preserved.

Details

Base model: Qwen/Qwen3.8-27B
Architecture: Qwen3_5ForCausalLM
Parameters: 27.32B
Layers: 64 · Hidden size: 5120
MTP head: preserved
Weights: bfloat16

Attribution

This model is a derivative of Qwen/Qwen3.8-27B by the Qwen team, released under the apache-2.0 license. All credit for the underlying weights and capabilities belongs to the original authors; this repository only removes modalities, it does not add new training.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("OnlyTextLLMs/Qwen3.8-27B-OnlyText")
tokenizer = AutoTokenizer.from_pretrained("OnlyTextLLMs/Qwen3.8-27B-OnlyText")