Instructions to use DongfuJiang/prm_version3_full_hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DongfuJiang/prm_version3_full_hf with Transformers:

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

pipe = pipeline("text-generation", model="DongfuJiang/prm_version3_full_hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DongfuJiang/prm_version3_full_hf")
model = AutoModelForCausalLM.from_pretrained("DongfuJiang/prm_version3_full_hf")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DongfuJiang/prm_version3_full_hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DongfuJiang/prm_version3_full_hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DongfuJiang/prm_version3_full_hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DongfuJiang/prm_version3_full_hf

SGLang

How to use DongfuJiang/prm_version3_full_hf 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 "DongfuJiang/prm_version3_full_hf" \
    --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": "DongfuJiang/prm_version3_full_hf",
		"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 "DongfuJiang/prm_version3_full_hf" \
        --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": "DongfuJiang/prm_version3_full_hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DongfuJiang/prm_version3_full_hf with Docker Model Runner:
```
docker model run hf.co/DongfuJiang/prm_version3_full_hf
```

prm_version3_full_hf

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the prm_conversations_prm_version3_math+webinstructsub-mcq+webinstructsub-oe+apps+gsm_mix_ref_hf dataset. It achieves the following results on the evaluation set:

Loss: 0.1166

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
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: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.1961	0.0461	500	0.2069
0.192	0.0921	1000	0.1930
0.1963	0.1382	1500	0.1833
0.1701	0.1843	2000	0.1748
0.1647	0.2303	2500	0.1687
0.1507	0.2764	3000	0.1630
0.1421	0.3225	3500	0.1579
0.1403	0.3685	4000	0.1528
0.1557	0.4146	4500	0.1485
0.1536	0.4607	5000	0.1441
0.1344	0.5067	5500	0.1399
0.1195	0.5528	6000	0.1355
0.1209	0.5989	6500	0.1316
0.137	0.6450	7000	0.1284
0.117	0.6910	7500	0.1253
0.116	0.7371	8000	0.1228
0.1259	0.7832	8500	0.1206
0.1147	0.8292	9000	0.1187
0.1175	0.8753	9500	0.1175
0.1117	0.9214	10000	0.1168
0.1133	0.9674	10500	0.1166

Framework versions

Transformers 4.45.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 2

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for DongfuJiang/prm_version3_full_hf

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2751)

this model