Instructions to use SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0 with Transformers:

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

pipe = pipeline("text-generation", model="SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0")
model = AutoModelForCausalLM.from_pretrained("SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_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 SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_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": "SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0

SGLang

How to use SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_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 "SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_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": "SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_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 "SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_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": "SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0 with Docker Model Runner:
```
docker model run hf.co/SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0
```

IRL_iter0_best_of_16_spin_iter0_epoch_5_saving

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the d, the a, the t, the a, the _, the g, the e, the n, the e, the r, the a, the t, the e, the d, the /, the s, the p, the i, the n, the _, the i, the t, the e, the r, the 0, the _, the b, the e, the s, the t, the _, the o, the f, the _, the 1, the 6, the /, the t, the o, the p, the 1, the _, the s, the e, the l, the e, the c, the t, the e, the d, the _, the I, the R, the L, the _, the r, the e, the w, the a, the r, the d, the _, the s, the e, the l, the e, the c, the t, the e and the d datasets. It achieves the following results on the evaluation set:

Loss: 0.0396

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-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.0895	1.0	79	0.7223
0.6454	2.0	158	0.3317
0.2926	3.0	237	0.1293
0.1048	4.0	316	0.0542
0.0465	5.0	395	0.0396

Framework versions

Transformers 4.44.2
Pytorch 2.1.2+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for SiliangZ/IRL_Iter0_Policy_Epoch5_RM_Data_SPIN_Iter0

Base model

mistralai/Mistral-7B-v0.1

Finetuned

alignment-handbook/zephyr-7b-sft-full

Finetuned

(393)

this model