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)

# pip install -U transformers accelerate
# 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=256)
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
```

Jaidchen commited on Sep 9

Commit

2494d5e

verified ·

1 Parent(s): c6f2e04

Update README.md

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README.md +1 -25

README.md CHANGED Viewed

@@ -21,12 +21,7 @@ tags:
 # Focus-Red-Int4
-4-bit quantization of [Focus-Red](https://huggingface.co/Jaidchen/Focus-Red), itself a text-only repackaging of [Qwen 3.8 27B](https://huggingface.co/Qwen/Qwen3.8-27B).
-- 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
@@ -100,24 +95,6 @@ tags:
   <td>random sampling</td>
   <td>greedy/deterministic</td>
 </tr>
-<tr>
-  <td>sampling parameters</td>
-  <td>
-    <div style='font-family: Jaidevka Code, JetBrains Mono, monospace; line-height: initial'>
-      <span style='color: hsl(from currentColor 0 100% l)'>do_sample</span>: <span style='color: hsl(from currentColor 50 80% l)'>true</span><br>
-      <span style='color: hsl(from currentColor 0 100% l)'>temperature</span>: <span style='color: hsl(from currentColor 50 80% l)'>1.0</span><br>
-      <span style='color: hsl(from currentColor 0 100% l)'>top_k</span>: <span style='color: hsl(from currentColor 50 80% l)'>20</span><br>
-      <span style='color: hsl(from currentColor 0 100% l)'>top_p</span>: <span style='color: hsl(from currentColor 50 80% l)'>0.95</span></div>
-  </td>
-  <td>
-    <div style='font-family: Jaidevka Code, JetBrains Mono, monospace; line-height: initial'>
-      <span style='color: hsl(from currentColor 0 100% l)'>do_sample</span>: <span style='color: hsl(from currentColor 50 80% l)'>false</span><br>
-      <span style='color: hsl(from currentColor 0 100% l)'>temperature</span>: <span style='color: hsl(from currentColor 50 80% l)'>0</span><br>
-      <span style='color: hsl(from currentColor 0 100% l)'>top_k</span>: <span style='color: hsl(from currentColor 50 80% l)'>1</span><br>
-      <span style='color: hsl(from currentColor 0 100% l)'>top_p</span>: <span style='color: hsl(from currentColor 50 80% l)'>1</span></div>
-    </div>
-  </td>
-</tr>
 <tr>
   <td>input modality</td>
   <td>text, image, video</td>
@@ -172,7 +149,6 @@ tags:
 ## 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

 # Focus-Red-Int4
+W4A16 G32 ASYM quantization of [Focus-Red](https://huggingface.co/Jaidchen/Focus-Red), itself a text-only repackaging of [Qwen 3.8 27B](https://huggingface.co/Qwen/Qwen3.8-27B).
 ## comparison
   <td>random sampling</td>
   <td>greedy/deterministic</td>
 </tr>
 <tr>
   <td>input modality</td>
   <td>text, image, video</td>
 ## caveats
 - model file not split, possibly causing issues if intended to be stored on an HDD from the previous century
 ## Jinja template