Instructions to use TIGER-Lab/MAmmoTH-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/MAmmoTH-Coder-7B with Transformers:

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

pipe = pipeline("text-generation", model="TIGER-Lab/MAmmoTH-Coder-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/MAmmoTH-Coder-7B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/MAmmoTH-Coder-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIGER-Lab/MAmmoTH-Coder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/MAmmoTH-Coder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/MAmmoTH-Coder-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TIGER-Lab/MAmmoTH-Coder-7B

SGLang

How to use TIGER-Lab/MAmmoTH-Coder-7B 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 "TIGER-Lab/MAmmoTH-Coder-7B" \
    --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": "TIGER-Lab/MAmmoTH-Coder-7B",
		"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 "TIGER-Lab/MAmmoTH-Coder-7B" \
        --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": "TIGER-Lab/MAmmoTH-Coder-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TIGER-Lab/MAmmoTH-Coder-7B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/MAmmoTH-Coder-7B
```

wenhu commited on Sep 13, 2023

Commit

3e42ca8

1 Parent(s): 3e24fc3

Update README.md

Browse files

Files changed (1) hide show

README.md +21 -1

README.md CHANGED Viewed

@@ -48,11 +48,31 @@ The models are evaluated using open-ended and multiple-choice math problems from
 | MAmmoTH       	| 70B  	| Llama-2    	| 76.7  	| 44.2 	| 61.4 	| 64.3    	| 61.7       	| 81.7  	| 55.3        	| 45.3    	| 58.6     	| 52.3      	| 58.6       	|
 ## Usage
 You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
 Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
 ## Intended Uses
 These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.

 | MAmmoTH       	| 70B  	| Llama-2    	| 76.7  	| 44.2 	| 61.4 	| 64.3    	| 61.7       	| 81.7  	| 55.3        	| 45.3    	| 58.6     	| 52.3      	| 58.6       	|
 ## Usage
 You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
 Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
+## Prompt Format
+If you want to do CoT:
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Response:
+```
+If you want to do PoT:
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{instruction} Let's write a program.
+### Response:
+```
 ## Intended Uses
 These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.