Instructions to use mnoukhov/pythia160m-sft-tldr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mnoukhov/pythia160m-sft-tldr with Transformers:

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

pipe = pipeline("text-generation", model="mnoukhov/pythia160m-sft-tldr")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mnoukhov/pythia160m-sft-tldr")
model = AutoModelForCausalLM.from_pretrained("mnoukhov/pythia160m-sft-tldr")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mnoukhov/pythia160m-sft-tldr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mnoukhov/pythia160m-sft-tldr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mnoukhov/pythia160m-sft-tldr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mnoukhov/pythia160m-sft-tldr

SGLang

How to use mnoukhov/pythia160m-sft-tldr 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 "mnoukhov/pythia160m-sft-tldr" \
    --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": "mnoukhov/pythia160m-sft-tldr",
		"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 "mnoukhov/pythia160m-sft-tldr" \
        --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": "mnoukhov/pythia160m-sft-tldr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mnoukhov/pythia160m-sft-tldr with Docker Model Runner:
```
docker model run hf.co/mnoukhov/pythia160m-sft-tldr
```

pythia160m-sft-tldr

This model is a fine-tuned version of EleutherAI/pythia-160m-deduped on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.7556

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.8454	0.4002	365	2.8609
2.7873	0.8004	730	2.7859
2.7641	1.2007	1095	2.7616
2.7486	1.6009	1460	2.7556

Framework versions

Transformers 4.41.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1

Downloads last month: 10

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for mnoukhov/pythia160m-sft-tldr

Base model

EleutherAI/pythia-160m-deduped

Finetuned

(52)

this model

Finetunes

3 models