Instructions to use DatPySci/pythia-1b-self-kto-iter0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DatPySci/pythia-1b-self-kto-iter0 with Transformers:

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

pipe = pipeline("text-generation", model="DatPySci/pythia-1b-self-kto-iter0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DatPySci/pythia-1b-self-kto-iter0")
model = AutoModelForCausalLM.from_pretrained("DatPySci/pythia-1b-self-kto-iter0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DatPySci/pythia-1b-self-kto-iter0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DatPySci/pythia-1b-self-kto-iter0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DatPySci/pythia-1b-self-kto-iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DatPySci/pythia-1b-self-kto-iter0

SGLang

How to use DatPySci/pythia-1b-self-kto-iter0 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 "DatPySci/pythia-1b-self-kto-iter0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DatPySci/pythia-1b-self-kto-iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "DatPySci/pythia-1b-self-kto-iter0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DatPySci/pythia-1b-self-kto-iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DatPySci/pythia-1b-self-kto-iter0 with Docker Model Runner:
```
docker model run hf.co/DatPySci/pythia-1b-self-kto-iter0
```

pythia-1b-kto-iter0-epoch1

This model is a fine-tuned version of DatPySci/pythia-1b-sft-full on the DatPySci/SPIN_iter0 dataset. It achieves the following results on the evaluation set:

Loss: 0.3355
Rewards/real: -0.0044
Rewards/generated: -1.1080
Rewards/accuracies: 0.9600
Rewards/margins: 1.1036
Logps/generated: -571.6245
Logps/real: -468.8171
Logits/generated: 0.2235
Logits/real: -0.2848

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-07
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/real	Rewards/generated	Rewards/accuracies	Rewards/margins	Logps/generated	Logps/real	Logits/generated	Logits/real
0.4016	0.38	300	0.3914	0.0270	-0.8143	0.9320	0.8413	-568.6872	-468.5030	0.2573	-0.2517
0.3451	0.77	600	0.3355	-0.0044	-1.1080	0.9600	1.1036	-571.6245	-468.8171	0.2235	-0.2848

Framework versions

Transformers 4.38.1
Pytorch 2.2.1
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 3

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for DatPySci/pythia-1b-self-kto-iter0

Base model

DatPySci/pythia-1b-sft-full

Finetuned

(2)

this model

Finetunes

1 model