Instructions to use Tverous/sft-trl-claim-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tverous/sft-trl-claim-128 with Transformers:

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

pipe = pipeline("text-generation", model="Tverous/sft-trl-claim-128")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Tverous/sft-trl-claim-128")
model = AutoModelForMultimodalLM.from_pretrained("Tverous/sft-trl-claim-128")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tverous/sft-trl-claim-128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tverous/sft-trl-claim-128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tverous/sft-trl-claim-128",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Tverous/sft-trl-claim-128

SGLang

How to use Tverous/sft-trl-claim-128 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 "Tverous/sft-trl-claim-128" \
    --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": "Tverous/sft-trl-claim-128",
		"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 "Tverous/sft-trl-claim-128" \
        --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": "Tverous/sft-trl-claim-128",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Tverous/sft-trl-claim-128 with Docker Model Runner:
```
docker model run hf.co/Tverous/sft-trl-claim-128
```

sft-trl-claim-128

This model is a fine-tuned version of EleutherAI/gpt-j-6b on the anli dataset. It achieves the following results on the evaluation set:

Loss: 0.3201

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.4364	0.08	1000	1.8760
0.9805	0.16	2000	1.2595
0.6629	0.24	3000	1.2970
0.4647	0.32	4000	0.9789
0.4579	0.4	5000	0.8591
0.383	0.48	6000	0.8866
0.4915	0.56	7000	0.4281
0.4139	0.64	8000	0.3946
0.2563	0.72	9000	0.3653
0.3179	0.8	10000	0.3528
0.4199	0.88	11000	0.3602
0.3877	0.96	12000	0.3457
0.2332	1.04	13000	0.3882
0.3817	1.11	14000	0.3604
0.2734	1.19	15000	0.3613
0.213	1.27	16000	0.3722
0.3154	1.35	17000	0.3378
0.2258	1.43	18000	0.3117
0.3198	1.51	19000	0.3213
0.2959	1.59	20000	0.3050
0.2588	1.67	21000	0.3190
0.2279	1.75	22000	0.3065
0.2988	1.83	23000	0.3077
0.3701	1.91	24000	0.3092
0.281	1.99	25000	0.3038
0.1743	2.07	26000	0.3542
0.1374	2.15	27000	0.3550
0.1282	2.23	28000	0.3386
0.1757	2.31	29000	0.3489
0.1371	2.39	30000	0.3316
0.1689	2.47	31000	0.3291
0.1882	2.55	32000	0.3292
0.1685	2.63	33000	0.3196
0.1775	2.71	34000	0.3320
0.1963	2.79	35000	0.3278
0.1733	2.87	36000	0.3221
0.1503	2.95	37000	0.3201

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

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Tverous
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sft-trl-claim-128

sft-trl-claim-128

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train Tverous/sft-trl-claim-128