Instructions to use koshirowada/pythia_160m_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koshirowada/pythia_160m_sft with Transformers:

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

pipe = pipeline("text-generation", model="koshirowada/pythia_160m_sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("koshirowada/pythia_160m_sft")
model = AutoModelForCausalLM.from_pretrained("koshirowada/pythia_160m_sft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use koshirowada/pythia_160m_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "koshirowada/pythia_160m_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "koshirowada/pythia_160m_sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/koshirowada/pythia_160m_sft

SGLang

How to use koshirowada/pythia_160m_sft 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 "koshirowada/pythia_160m_sft" \
    --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": "koshirowada/pythia_160m_sft",
		"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 "koshirowada/pythia_160m_sft" \
        --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": "koshirowada/pythia_160m_sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use koshirowada/pythia_160m_sft with Docker Model Runner:
```
docker model run hf.co/koshirowada/pythia_160m_sft
```

pythia_160m_sft / README.md

koshirowada

Update README.md

4693b60 verified over 1 year ago

preview code

raw

history blame contribute delete

3.12 kB

metadata

library_name: transformers
license: apache-2.0
base_model: EleutherAI/pythia-160m
tags:
  - generated_from_trainer
model-index:
  - name: pythia_160m_sft
    results: []
datasets:
  - tatsu-lab/alpaca_farm

pythia_160m_sft

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

Loss: 1.9831

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: 2e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.2935	0.0889	100	2.1426
2.153	0.1778	200	2.0977
2.1432	0.2667	300	2.0771
2.1131	0.3556	400	2.0633
2.0885	0.4444	500	2.0510
2.0956	0.5333	600	2.0403
2.0647	0.6222	700	2.0354
2.0498	0.7111	800	2.0273
2.0317	0.8	900	2.0202
2.0226	0.8889	1000	2.0150
1.992	0.9778	1100	2.0114
1.9639	1.0667	1200	2.0088
1.9302	1.1556	1300	2.0051
1.9381	1.2444	1400	2.0028
1.9595	1.3333	1500	2.0009
1.9325	1.4222	1600	1.9998
1.9481	1.5111	1700	1.9981
1.9572	1.6	1800	1.9956
1.9456	1.6889	1900	1.9944
1.9565	1.7778	2000	1.9922
1.9507	1.8667	2100	1.9905
1.9247	1.9556	2200	1.9881
1.8998	2.0444	2300	1.9874
1.9102	2.1333	2400	1.9873
1.8842	2.2222	2500	1.9876
1.876	2.3111	2600	1.9863
1.9001	2.4	2700	1.9856
1.8725	2.4889	2800	1.9859
1.868	2.5778	2900	1.9845
1.8803	2.6667	3000	1.9844
1.9002	2.7556	3100	1.9838
1.8941	2.8444	3200	1.9839
1.8548	2.9333	3300	1.9831

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3