Instructions to use Tobias1726/shadowfox-fixed-qk5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tobias1726/shadowfox-fixed-qk5 with Transformers:

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

pipe = pipeline("text-generation", model="Tobias1726/shadowfox-fixed-qk5", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tobias1726/shadowfox-fixed-qk5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tobias1726/shadowfox-fixed-qk5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tobias1726/shadowfox-fixed-qk5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tobias1726/shadowfox-fixed-qk5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tobias1726/shadowfox-fixed-qk5

SGLang

How to use Tobias1726/shadowfox-fixed-qk5 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 "Tobias1726/shadowfox-fixed-qk5" \
    --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": "Tobias1726/shadowfox-fixed-qk5",
		"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 "Tobias1726/shadowfox-fixed-qk5" \
        --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": "Tobias1726/shadowfox-fixed-qk5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tobias1726/shadowfox-fixed-qk5 with Docker Model Runner:
```
docker model run hf.co/Tobias1726/shadowfox-fixed-qk5
```

shadowfox-fixed-qk5 / tokenizer.json

Tobias1726

shadowfox-fixed-v1: perturb_clone of silx-ai/Quasar-3B-A1B-Preview + zeroed LatentMemoryModule (fix bf16 NaN at seq>=64)

b3c1e81 verified 4 months ago

Download

History Blame Contribute Delete

12.8 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 90cdfe25a6078132bfe69179f0ff319854304299935a36ec5891b0bc6212d2b4
Size of remote file:: 12.8 MB
SHA256:: fe000e3ed39ed12b8d2481d527d44f93c65d37e87645d2dcc80d1bf9d50d2927

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.