Instructions to use kshitijthakkar/poc-pipeline-e2e-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kshitijthakkar/poc-pipeline-e2e-test with Transformers:

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

pipe = pipeline("text-generation", model="kshitijthakkar/poc-pipeline-e2e-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kshitijthakkar/poc-pipeline-e2e-test")
model = AutoModelForCausalLM.from_pretrained("kshitijthakkar/poc-pipeline-e2e-test", 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 kshitijthakkar/poc-pipeline-e2e-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kshitijthakkar/poc-pipeline-e2e-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kshitijthakkar/poc-pipeline-e2e-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kshitijthakkar/poc-pipeline-e2e-test

SGLang

How to use kshitijthakkar/poc-pipeline-e2e-test 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 "kshitijthakkar/poc-pipeline-e2e-test" \
    --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": "kshitijthakkar/poc-pipeline-e2e-test",
		"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 "kshitijthakkar/poc-pipeline-e2e-test" \
        --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": "kshitijthakkar/poc-pipeline-e2e-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kshitijthakkar/poc-pipeline-e2e-test with Docker Model Runner:
```
docker model run hf.co/kshitijthakkar/poc-pipeline-e2e-test
```

Qwen3 MoE (Mixture of Experts) — 39M Parameters

Custom Qwen3 MoE model trained with pipeline parallelism.

Model Details

Property	Value
Total Parameters	39,388,928
Architecture	MoE (Mixture of Experts)
Hidden Size	128
Num Layers	2
Attention Heads	4
Context Length	512
Vocab Size	151,936
Num Experts	4
Top-K Experts	2
MoE Hidden Dim	128

Evaluation Results

Metric	Value
val_loss	0.3217
val_perplexity	1.3795
train_loss	0.2418
step	2000

Usage

import torch
from safetensors.torch import load_file

# Load model weights
state_dict = load_file("model.safetensors")

Training

Trained using pipeline parallelism with the multi_gpu_pretraining framework.

Downloads last month: 35

Safetensors

Model size

39.4M params

Tensor type

F32

BF16