Instructions to use HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath with Transformers:

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

pipe = pipeline("text-generation", model="HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath")
model = AutoModelForCausalLM.from_pretrained("HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath

SGLang

How to use HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath 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 "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath" \
    --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": "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath",
		"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 "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath" \
        --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": "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath with Docker Model Runner:
```
docker model run hf.co/HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath
```

HachiML commited on Apr 6, 2024

Commit

5ffe322

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1 Parent(s): c39b50e

Update README.md

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README.md CHANGED Viewed

@@ -78,7 +78,8 @@ Below are examples of responses from the model, demonstrating how the SkillTree
 ### Example 1:
 - **Input:** Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?\n
-- **Output:** ```
 Let's solve this problem using Python code.
 <llm-code>
 friends_sold_in_April = 48
@@ -95,7 +96,8 @@ Thus, in April and May, Natalia sold \boxed{72} clips in total.
 ### Example 2:
 - **Input:** ナタリアは4月に48人の友人にクリップを売り、5月にはその半分の数のクリップを売った。ナタリアが4月と5月に売ったクリップの数は？:\n
-- **Output:** ```
 Let's solve this problem using Python code.
 <llm-code>
 clip_count = 48

 ### Example 1:
 - **Input:** Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?\n
+- **Output:**
+```
 Let's solve this problem using Python code.
 <llm-code>
 friends_sold_in_April = 48
 ### Example 2:
 - **Input:** ナタリアは4月に48人の友人にクリップを売り、5月にはその半分の数のクリップを売った。ナタリアが4月と5月に売ったクリップの数は？:\n
+- **Output:**
+```
 Let's solve this problem using Python code.
 <llm-code>
 clip_count = 48