Instructions to use Symbol-LLM/ENVISIONS_7B_math_iter10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Symbol-LLM/ENVISIONS_7B_math_iter10 with Transformers:

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

pipe = pipeline("text-generation", model="Symbol-LLM/ENVISIONS_7B_math_iter10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Symbol-LLM/ENVISIONS_7B_math_iter10")
model = AutoModelForCausalLM.from_pretrained("Symbol-LLM/ENVISIONS_7B_math_iter10")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Symbol-LLM/ENVISIONS_7B_math_iter10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Symbol-LLM/ENVISIONS_7B_math_iter10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Symbol-LLM/ENVISIONS_7B_math_iter10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Symbol-LLM/ENVISIONS_7B_math_iter10

SGLang

How to use Symbol-LLM/ENVISIONS_7B_math_iter10 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 "Symbol-LLM/ENVISIONS_7B_math_iter10" \
    --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": "Symbol-LLM/ENVISIONS_7B_math_iter10",
		"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 "Symbol-LLM/ENVISIONS_7B_math_iter10" \
        --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": "Symbol-LLM/ENVISIONS_7B_math_iter10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Symbol-LLM/ENVISIONS_7B_math_iter10 with Docker Model Runner:
```
docker model run hf.co/Symbol-LLM/ENVISIONS_7B_math_iter10
```

Symbol-LLM commited on Jul 8, 2024

Commit

2ba5fe1

verified ·

1 Parent(s): e5f7115

Update README.md

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@@ -17,7 +17,16 @@ Code Repo: https://github.com/xufangzhi/ENVISIONS
 ## Note
-The work is under review.
 ## Citation

 ## Note
+The self-training process is based on LLaMA2-Chat model serieses and powered by ENVISIONS. The work is still under review.
+## Prompt for Zero-shot Evaluation
+```markdown
+Write Python code to solve the question.
+The question is: <question>
+The solution code is:
+```
 ## Citation