Instructions to use aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold with Transformers:

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

pipe = pipeline("text-generation", model="aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold")
model = AutoModelForCausalLM.from_pretrained("aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold", 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 aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold

SGLang

How to use aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold 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 "aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold" \
    --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": "aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold",
		"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 "aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold" \
        --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": "aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold with Docker Model Runner:
```
docker model run hf.co/aneeshm44/gpt-oss-104b-dynamic-pruning-99-5-threshold
```

gpt-oss-104b-dynamic-pruning-99-5-threshold / model-00001-of-00014.safetensors

aneeshm44

Upload model-00001-of-00014.safetensors with huggingface_hub

24a1087 verified 4 months ago

Download

History Blame Contribute Delete

3.66 GB

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:: f32c67d81ab0ba4e15d8beaef530e77f5ecd92a719ab5491f11dabff0afbe7e4
Size of remote file:: 3.66 GB
SHA256:: 5eab0b7a3efe2cac59315fe6fb2e569b3b435bd11b4169796668d5365a96b429

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