Instructions to use Jab1718/qwen3.8-flash-coder-26gb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jab1718/qwen3.8-flash-coder-26gb with Transformers:

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

pipe = pipeline("text-generation", model="Jab1718/qwen3.8-flash-coder-26gb")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jab1718/qwen3.8-flash-coder-26gb")
model = AutoModelForCausalLM.from_pretrained("Jab1718/qwen3.8-flash-coder-26gb", 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 Jab1718/qwen3.8-flash-coder-26gb with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jab1718/qwen3.8-flash-coder-26gb"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jab1718/qwen3.8-flash-coder-26gb",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jab1718/qwen3.8-flash-coder-26gb

SGLang

How to use Jab1718/qwen3.8-flash-coder-26gb 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 "Jab1718/qwen3.8-flash-coder-26gb" \
    --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": "Jab1718/qwen3.8-flash-coder-26gb",
		"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 "Jab1718/qwen3.8-flash-coder-26gb" \
        --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": "Jab1718/qwen3.8-flash-coder-26gb",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jab1718/qwen3.8-flash-coder-26gb with Docker Model Runner:
```
docker model run hf.co/Jab1718/qwen3.8-flash-coder-26gb
```

I was hoping for a prune/distill of this model!

by sometimesanotion - opened 2 days ago

Discussion

sometimesanotion

2 days ago

Thank you for creating this. I'm very much looking forward to trying it! Will you be publishing an expert index mapping? Have you considered using Unsloth's or Mudler's quantization schemes to make GGUFs of this model?

Jab1718

Owner about 15 hours ago

•

edited about 15 hours ago

Tks for your appreciation for my project, those are just demo checkpoints btw, im aiming to compact the model to just 20gb vram while maintaining ~98% of the coding ability

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment