Instructions to use avneeshjadhav04/llm-from-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use avneeshjadhav04/llm-from-scratch with Transformers:

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

pipe = pipeline("text-generation", model="avneeshjadhav04/llm-from-scratch")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("avneeshjadhav04/llm-from-scratch", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use avneeshjadhav04/llm-from-scratch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "avneeshjadhav04/llm-from-scratch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "avneeshjadhav04/llm-from-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/avneeshjadhav04/llm-from-scratch

SGLang

How to use avneeshjadhav04/llm-from-scratch 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 "avneeshjadhav04/llm-from-scratch" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "avneeshjadhav04/llm-from-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "avneeshjadhav04/llm-from-scratch" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "avneeshjadhav04/llm-from-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use avneeshjadhav04/llm-from-scratch with Docker Model Runner:
```
docker model run hf.co/avneeshjadhav04/llm-from-scratch
```

avneeshjadhav04 commited on 14 days ago

Commit

4032d74

verified ·

1 Parent(s): 3d18b0d

Update README.md

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README.md +6 -2

README.md CHANGED Viewed

@@ -16,8 +16,11 @@ datasets:
 ---
 # LLM from Scratch (124M)
 A clean, **from-scratch implementation** of a 124M-parameter decoder-only Transformer in PyTorch. No `nn.Transformer`, no shortcuts — every layer (attention, FFN, LayerNorm, embeddings) is built manually. Trained on **FineWeb-Edu** with mixed precision, gradient accumulation, and automatic checkpoint resume.
 🚀 **Live Demo**: [https://avneeshjadhav04--llm-api.modal.run](https://avneeshjadhav04--llm-api.modal.run)
 📂 **Training Code**: [github.com/avneeshjadhav04/llm-from-scratch](https://github.com/avneeshjadhav04/llm-from-scratch)
 ## Model Description
 This is a **GPT-2-style causal language model** trained entirely from random initialization on the FineWeb-Edu dataset. It was built as an educational and portfolio project to demonstrate deep understanding of Transformer internals and large-scale training loops.
 | Property | Value |
@@ -78,10 +81,11 @@ If you use this model or code, please cite:
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/avneeshjadhav04/llm-from-scratch}}
 }
-Acknowledgments
 - Inspired by Andrej Karpathy's nanoGPT (https://github.com/karpathy/nanoGPT)
 - Transformer architecture from Attention Is All You Need (https://arxiv.org/abs/1706.03762)
 - FineWeb-Edu dataset by HuggingFace (https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
-License
 - Code: MIT License
 - Dataset: FineWeb-Edu is released under ODC-By v1.0 (https://opendatacommons.org/licenses/by/1-0/)

 ---
 # LLM from Scratch (124M)
 A clean, **from-scratch implementation** of a 124M-parameter decoder-only Transformer in PyTorch. No `nn.Transformer`, no shortcuts — every layer (attention, FFN, LayerNorm, embeddings) is built manually. Trained on **FineWeb-Edu** with mixed precision, gradient accumulation, and automatic checkpoint resume.
 🚀 **Live Demo**: [https://avneeshjadhav04--llm-api.modal.run](https://avneeshjadhav04--llm-api.modal.run)
 📂 **Training Code**: [github.com/avneeshjadhav04/llm-from-scratch](https://github.com/avneeshjadhav04/llm-from-scratch)
 ## Model Description
 This is a **GPT-2-style causal language model** trained entirely from random initialization on the FineWeb-Edu dataset. It was built as an educational and portfolio project to demonstrate deep understanding of Transformer internals and large-scale training loops.
 | Property | Value |
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/avneeshjadhav04/llm-from-scratch}}
 }
+```
+# Acknowledgments
 - Inspired by Andrej Karpathy's nanoGPT (https://github.com/karpathy/nanoGPT)
 - Transformer architecture from Attention Is All You Need (https://arxiv.org/abs/1706.03762)
 - FineWeb-Edu dataset by HuggingFace (https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
+# License
 - Code: MIT License
 - Dataset: FineWeb-Edu is released under ODC-By v1.0 (https://opendatacommons.org/licenses/by/1-0/)