Instructions to use HanningZhang/Llama3.1-Math-PRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HanningZhang/Llama3.1-Math-PRM with Transformers:

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

pipe = pipeline("text-generation", model="HanningZhang/Llama3.1-Math-PRM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HanningZhang/Llama3.1-Math-PRM")
model = AutoModelForCausalLM.from_pretrained("HanningZhang/Llama3.1-Math-PRM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HanningZhang/Llama3.1-Math-PRM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HanningZhang/Llama3.1-Math-PRM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HanningZhang/Llama3.1-Math-PRM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HanningZhang/Llama3.1-Math-PRM

SGLang

How to use HanningZhang/Llama3.1-Math-PRM 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 "HanningZhang/Llama3.1-Math-PRM" \
    --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": "HanningZhang/Llama3.1-Math-PRM",
		"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 "HanningZhang/Llama3.1-Math-PRM" \
        --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": "HanningZhang/Llama3.1-Math-PRM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HanningZhang/Llama3.1-Math-PRM with Docker Model Runner:
```
docker model run hf.co/HanningZhang/Llama3.1-Math-PRM
```

HanningZhang commited on Dec 20, 2024

Commit

666a53a

verified ·

1 Parent(s): 85bc039

Upload LlamaForCausalLM

Browse files

Files changed (6) hide show

config.json +2 -3
generation_config.json +1 -1
model-00001-of-00004.safetensors +1 -1
model-00002-of-00004.safetensors +1 -1
model-00003-of-00004.safetensors +1 -1
model-00004-of-00004.safetensors +1 -1

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "hallucination_test/reward_llama_31_8b_autoregressive_ce2_5e-7lr",
   "architectures": [
     "LlamaForCausalLM"
   ],
@@ -7,7 +7,6 @@
   "attention_dropout": 0.0,
   "bos_token_id": 128000,
   "eos_token_id": 128001,
-  "head_dim": 128,
   "hidden_act": "silu",
   "hidden_size": 4096,
   "initializer_range": 0.02,
@@ -31,7 +30,7 @@
   "rope_theta": 500000.0,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.46.0",
   "use_cache": true,
   "vocab_size": 128256
 }

 {
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   "architectures": [
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   "attention_dropout": 0.0,
   "bos_token_id": 128000,
   "eos_token_id": 128001,
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   "initializer_range": 0.02,
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   "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.4",
   "use_cache": true,
   "vocab_size": 128256
 }

generation_config.json CHANGED Viewed

@@ -5,5 +5,5 @@
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   "temperature": 0.6,
   "top_p": 0.9,
-  "transformers_version": "4.46.0"
 }

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