Instructions to use mehta/CooperLM-354M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mehta/CooperLM-354M with Transformers:

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

pipe = pipeline("text-generation", model="mehta/CooperLM-354M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mehta/CooperLM-354M")
model = AutoModelForCausalLM.from_pretrained("mehta/CooperLM-354M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mehta/CooperLM-354M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mehta/CooperLM-354M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mehta/CooperLM-354M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mehta/CooperLM-354M

SGLang

How to use mehta/CooperLM-354M 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 "mehta/CooperLM-354M" \
    --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": "mehta/CooperLM-354M",
		"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 "mehta/CooperLM-354M" \
        --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": "mehta/CooperLM-354M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mehta/CooperLM-354M with Docker Model Runner:
```
docker model run hf.co/mehta/CooperLM-354M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 CooperLM-354M

CooperLM-354M is a 354 million parameter GPT-2 based language model trained from scratch on a filtered subset of Wikipedia, BookCorpus, and OpenWebText. It was created as a toy project to explore end-to-end LLM training using Hugging Face’s Transformers and Datasets libraries.

Github Repo: https://github.com/daniel-mehta/CooperLM-354M

🧱 Architecture

GPT-2 with 24 layers, 16 heads, 1024 hidden size
256-token context window
Trained for 1 epoch on 100k samples (~1.2M sequences)

📊 Training Details

Setting	Value
Model Type	GPT2LMHeadModel
Epochs	1
Precision	fp16
Batch Size (effective)	16
GPU	RTX 4060
Final Eval Loss	5.63
Perplexity	~263

📥 Usage

from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("mehta/CooperLM-354M")
tokenizer = GPT2TokenizerFast.from_pretrained("mehta/CooperLM-354M")

prompt = "In a distant future,"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.9, top_p=0.95)

print(tokenizer.decode(output[0], skip_special_tokens=True))

📝License

MIT

Downloads last month: 6

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for mehta/CooperLM-354M

Base model

openai-community/gpt2

Finetuned

(2159)

this model

Quantizations

2 models