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

Libraries

How to use Jaidchen/Focus with Transformers:

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

pipe = pipeline("text-generation", model="Jaidchen/Focus")
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("Jaidchen/Focus")
model = AutoModelForCausalLM.from_pretrained("Jaidchen/Focus", 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 Jaidchen/Focus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jaidchen/Focus"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jaidchen/Focus

SGLang

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

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

Focus

repackaged Qwen 3.6 27B with a simplified architecture and minor opinionated improvements

all vision-related components removed
reduced storage and memory, faster inference
zero loss of output quality

comparison

	Qwen 3.6 27B	Focus
author	Alibaba Qwen	Jaid
repository	Qwen/Qwen3.6-27B	Jaidchen/Focus
architecture	`qwen3_5`	`qwen3_5_text`
Transformers handler	`Qwen3_5ForConditionalGeneration`	`Qwen3_5ForCausalLM`
tensor entries	1199	866
tensor type	bf16	bf16
parameters	27 781 427 952	27 320 697 856
active	100%	100%
vocabulary size	248 320	248 320
context size	262 144	262 144
MTP	integrated	integrated
sampling strategy	random sampling	greedy/deterministic
sampling parameters	do_sample: true temperature: 0.6 top_k: 20 top_p: 0.95	do_sample: false temperature: 0 top_k: 1 top_p: 1
input modality	text, image, video	text
repository size	55 586 107 940	54 659 211 447
model size	55 562 855 904	54 641 395 712
splits	15	none
Jinja template	Qwen original	Qwen original + Unsloth tweaks + Froggeric tweaks + unimodality patch + further custom tweaks

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 changes

chat.jinja is reproducibly built from an untouched upstream template plus the ordered patch stack in jinja_build/. jinja_build/build.ps1 applies the patches lexicographically and overwrites the final chat.jinja.

base: Qwen/Qwen3.6-27B original
- commit 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9
- SHA-256 E84F32A23FDDA27689F868AA4A1A5621F41133E51A48D7F3EFCBEA2839574259
adopted tweaks from Unsloth Qwen3.6
- Added developer role as alias for system. (lines 45–57)
- Merged leading system/developer messages into a single policy message. (lines 45–57, lines 67–75)
- Allowed histories without a normal human query. (lines 76–86)
- Made mapping-argument rendering portable by avoiding |items. (lines 122–130)
adopted tweaks from Froggeric version
- Added preserve_thinking option to retain historical reasoning. (line 8, lines 225–230)
- Retained mid-conversation system/developer messages as Qwen system turns. (lines 165–182)
- Allowed string-valued message.thinking as fallback for historical reasoning. (lines 183–197)
- Applied boundary-aware </think> parsing. (lines 198–223)
- Avoided synthesizing empty historical thinking blocks. (lines 225–230)
- Supported both wrapped and direct tool calls, but never null-wrapped. (lines 231–237)
- Preserved non-empty string tool arguments. (lines 261–280)
- Used direct message indexing for tool-response grouping instead of loop.previtem/loop.nextitem. (line 165, lines 286–318)
custom tweaks
- Merged any number of leading system/developer messages, generalizing Unsloth's two-message merge. (lines 45–57)
- Applied boundary-aware </think> parsing but excluded Froggeric's malformed-tag recovery. (lines 198–223)
- Raised an error containing the offending role instead of Qwen's generic unknown-role error. (lines 143–144)
- Used direct message indexing for tool-response grouping without Froggeric's error-escalation state. (lines 286–318)
- Guarded undefined tools/tool_calls and handled non-mapping content items defensively.
- Adopted Qwen's proposed continue_final_message fix for partial assistant prefills. (lines 130–132)
unimodality patch
- Removed Qwen's vision-token machinery and rendered image/video content as [image] and [video]. (lines 1–41)

license

Apache 2.0 – inherited from Qwen 3.6 27B

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for Jaidchen/Focus

Base model

Qwen/Qwen3.6-27B

Finetuned

(425)

this model