Instructions to use Jaidchen/Focus-Red-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jaidchen/Focus-Red-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="Jaidchen/Focus-Red-Int4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jaidchen/Focus-Red-Int4")
model = AutoModelForCausalLM.from_pretrained("Jaidchen/Focus-Red-Int4", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jaidchen/Focus-Red-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jaidchen/Focus-Red-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jaidchen/Focus-Red-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jaidchen/Focus-Red-Int4

SGLang

How to use Jaidchen/Focus-Red-Int4 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 "Jaidchen/Focus-Red-Int4" \
    --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": "Jaidchen/Focus-Red-Int4",
		"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 "Jaidchen/Focus-Red-Int4" \
        --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": "Jaidchen/Focus-Red-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jaidchen/Focus-Red-Int4 with Docker Model Runner:
```
docker model run hf.co/Jaidchen/Focus-Red-Int4
```

Focus-Red-Int4

4-bit quantization of Focus-Red, itself a text-only repackaging of Qwen 3.8 27B.

W4A16, group size 32, asymmetric weights
compressed-tensors / pack-quantized format
selected linear-attention projections and lm_head remain BF16
single model.safetensors file

comparison

	Qwen 3.8 27B	Focus-Red-Int4
author	Alibaba Qwen	Jaid
repository	Qwen/Qwen3.8-27B	Jaidchen/Focus-Red-Int4
architecture	`qwen3_5`	`qwen3_5_text`
Transformers handler	`Qwen3_5ForConditionalGeneration`	`Qwen3_5ForCausalLM`
tensor entries	1199	2051
tensor type	bf16	W4A16 G32 asymmetric + selected BF16
parameters	27 781 427 952	26 895 998 464
active	100%	100%
vocabulary size	248 320	248 320
context size	262 144	262 144
MTP	integrated	detached → Focus-Red-MTP
sampling strategy	random sampling	greedy/deterministic
sampling parameters	do_sample: true temperature: 1.0 top_k: 20 top_p: 0.95	do_sample: false temperature: 0 top_k: 1 top_p: 1
input modality	text, image, video	text
model size	55 562 855 904	19 202 352 336 bytes on disk
splits	18	none
Jinja template	Qwen original	focus-chat-template dist build

quantization

method: compressed-tensors
format: pack-quantized
weights: 4-bit integer, group size 32, asymmetric
activations: unquantized / BF16
quantization status: compressed
model file: 19,202,352,336 bytes

pros

reduced storage needs
reduced loading time
reduced VRAM occupancy, thus more room for context
increased inference speed
simplified architecture, unlocking some further potential for optimizing low-level procedures

cons

legally blind
- Pictures and video frames can still be present in the context without crashing, but their contents are no longer interpreted by the model and won’t do anything else than waste space.
- If you occasionally rely on those capabilities, I suggest adding a consult tool to your harness that calls a vision-enabled subagent model like Gemini Flash or GPT.
reduced compatibility
- The simplified architecture is handled by the Qwen3_5ForCausalLM class which may not be included in your inference engine. In this case you would need to ask your agent or integrate it yourself.
  - Inference engines confirmed to have native support:
    - SGLang ≥ v0.5.17 (PR #32401)
    - vLLM ≥ v0.26.0 (PR #50210)
- The applied coercions may confuse your inference engine in case it has fixed expectations about the model’s architecture and thus lead to unpredictable behavior.

caveats

model file not split, possibly causing issues if intended to be stored on an HDD from the previous century
random sampling disabled by default, less suitable for long-form writing, entertainment and casual chat

Jinja template

This build uses dist/chat_template.jinja from jaidlab/focus-chat-template. The template is reproducibly built from Qwen/Qwen3.8-27B's pinned upstream template at commit 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0 plus the repository's ordered patch stack.

template SHA-256: 5c381ca45e9538c7a2406331b554ee7d62cf3d0b8c115f17687d4fdd5590a239
template size: 9,710 bytes

license

Apache 2.0 – inherited from Qwen 3.8 27B

Downloads last month: 142

Safetensors

Model size

27B params

Tensor type

BF16

I32

Model tree for Jaidchen/Focus-Red-Int4

Base model

Qwen/Qwen3.8-27B

Finetuned

Jaidchen/Focus-Red

Quantized

(1)

this model