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

Libraries

How to use rbtprograms/merged_mistral_base_math with Transformers:

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

pipe = pipeline("text-generation", model="rbtprograms/merged_mistral_base_math")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rbtprograms/merged_mistral_base_math")
model = AutoModelForCausalLM.from_pretrained("rbtprograms/merged_mistral_base_math")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbtprograms/merged_mistral_base_math with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rbtprograms/merged_mistral_base_math

SGLang

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

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

merged_2

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

dtype: float16
merge_method: linear
slices:
- sources:
  - layer_range: [0, 32]
    model:
      model:
        path: mistralai/Mistral-7B-v0.1
    parameters:
      weight: 1.0
  - layer_range: [0, 32]
    model:
      model:
        path: WizardLM/WizardMath-7B-V1.1
    parameters:
      weight: 0.0

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

F16

Model tree for rbtprograms/merged_mistral_base_math

WizardLMTeam/WizardMath-7B-V1.1

mistralai/Mistral-7B-v0.1

Merge model

this model

Paper for rbtprograms/merged_mistral_base_math

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8