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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jaidchen/Focus")
model = AutoModelForCausalLM.from_pretrained("Jaidchen/Focus")
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 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
```

Jaidchen commited on 12 days ago

Commit

6591d01

verified ·

1 Parent(s): aee6254

Update README.md

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README.md +8 -8

README.md CHANGED Viewed

@@ -38,16 +38,16 @@ rebuilt [Qwen 3.6 27B](https://huggingface.co/Qwen/Qwen3.6-27B) with a simplifie
   <td>1199</td>
   <td>866</td>
 </tr>
-<tr>
-  <td>parameters</td>
-  <td>27 781 427 952</td>
-  <td>27 320 697 856</td>
-</tr>
 <tr style='background: #5552; color: rgb(from currentColor r g b / 70%)'>
   <td>tensor type</td>
   <td>bf16</td>
   <td>bf16</td>
 </tr>
 <tr style='background: #5552; color: rgb(from currentColor r g b / 70%)'>
   <td>vocabulary size</td>
   <td>248 320</td>
@@ -72,7 +72,7 @@ rebuilt [Qwen 3.6 27B](https://huggingface.co/Qwen/Qwen3.6-27B) with a simplifie
   <a href='https://huggingface.co/docs/transformers/model_doc/qwen3_5#transformers.Qwen3_5ForCausalLM'><code>Qwen3_5ForCausalLM</code></a>
   </td>
 <tr>
-  <td>sampling</td>
   <td>random sampling</td>
   <td>greedy/deterministic</td>
 </tr>
@@ -86,7 +86,7 @@ rebuilt [Qwen 3.6 27B](https://huggingface.co/Qwen/Qwen3.6-27B) with a simplifie
   </td>
 </tr>
 <tr>
-  <td>modality</td>
   <td>text, image, video</td>
   <td>text</td>
 </tr>
@@ -98,7 +98,7 @@ rebuilt [Qwen 3.6 27B](https://huggingface.co/Qwen/Qwen3.6-27B) with a simplifie
 <tr>
   <td>splits</td>
   <td>11</td>
-  <td>–</td>
 </tr>
 <tr>
   <td>Jinja template</td>

   <td>1199</td>
   <td>866</td>
 </tr>
 <tr style='background: #5552; color: rgb(from currentColor r g b / 70%)'>
   <td>tensor type</td>
   <td>bf16</td>
   <td>bf16</td>
 </tr>
+<tr>
+  <td>parameters</td>
+  <td>27 781 427 952</td>
+  <td>27 320 697 856</td>
+</tr>
 <tr style='background: #5552; color: rgb(from currentColor r g b / 70%)'>
   <td>vocabulary size</td>
   <td>248 320</td>
   <a href='https://huggingface.co/docs/transformers/model_doc/qwen3_5#transformers.Qwen3_5ForCausalLM'><code>Qwen3_5ForCausalLM</code></a>
   </td>
 <tr>
+  <td>sampling strategy</td>
   <td>random sampling</td>
   <td>greedy/deterministic</td>
 </tr>
   </td>
 </tr>
 <tr>
+  <td>input modality</td>
   <td>text, image, video</td>
   <td>text</td>
 </tr>
 <tr>
   <td>splits</td>
   <td>11</td>
+  <td>none</td>
 </tr>
 <tr>
   <td>Jinja template</td>