Instructions to use AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF with Transformers:

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

pipe = pipeline("text-generation", model="AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF")
model = AutoModelForCausalLM.from_pretrained("AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF", device_map="auto")
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 Settings

vLLM

How to use AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF

SGLang

How to use AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF 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 "AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF" \
    --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": "AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF",
		"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 "AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF" \
        --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": "AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF with Docker Model Runner:
```
docker model run hf.co/AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF
```

TinyLlama-1.1B-IPO-PKU-SafeRLHF

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 18.4083
Rewards/chosen: -0.3421
Rewards/rejected: -0.4737
Rewards/accuracies: 0.7389
Rewards/margins: 0.1316
Logps/rejected: -6.7403
Logps/chosen: -5.4225
Logits/rejected: -2.2712
Logits/chosen: -2.4037

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
24.8672	0.0960	200	24.8184	-0.0031	-0.0050	0.7069	0.0018	-2.0534	-2.0334	-2.7185	-2.8480
24.1359	0.1920	400	24.2118	-0.0132	-0.0215	0.7351	0.0083	-2.2183	-2.1337	-2.6542	-2.7800
22.6582	0.2880	600	22.9068	-0.0523	-0.0760	0.7280	0.0237	-2.7638	-2.5252	-2.4992	-2.6281
21.2405	0.3840	800	19.5980	-0.2860	-0.3895	0.7207	0.1036	-5.8987	-4.8616	-2.3572	-2.4838
16.8004	0.4800	1000	18.8829	-0.3386	-0.4676	0.7299	0.1290	-6.6800	-5.3883	-2.2910	-2.4227
20.9951	0.5760	1200	18.5470	-0.3274	-0.4552	0.7343	0.1279	-6.5555	-5.2756	-2.2822	-2.4138
19.3603	0.6719	1400	18.4526	-0.3367	-0.4669	0.7367	0.1303	-6.6729	-5.3688	-2.2770	-2.4091
16.8747	0.7679	1600	18.4182	-0.3410	-0.4724	0.7383	0.1314	-6.7270	-5.4120	-2.2717	-2.4042
20.3795	0.8639	1800	18.4132	-0.3418	-0.4733	0.7370	0.1315	-6.7369	-5.4201	-2.2711	-2.4037
18.8851	0.9599	2000	18.4097	-0.3421	-0.4737	0.7381	0.1316	-6.7404	-5.4228	-2.2713	-2.4037

Framework versions

Transformers 4.44.2
Pytorch 2.11.0+cu130
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 18

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(580)

this model

Collection including AIPlans/TinyLlama-1.1B-IPO-PKU-SafeRLHF

TinyLlama RLHF Models

Collection

6 items • Updated May 16