Instructions to use Haleshot/Mathmate-7B-DELLA-ORPO-C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Haleshot/Mathmate-7B-DELLA-ORPO-C with Transformers:

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

pipe = pipeline("text-generation", model="Haleshot/Mathmate-7B-DELLA-ORPO-C")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Haleshot/Mathmate-7B-DELLA-ORPO-C", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Haleshot/Mathmate-7B-DELLA-ORPO-C with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Haleshot/Mathmate-7B-DELLA-ORPO-C"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haleshot/Mathmate-7B-DELLA-ORPO-C",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Haleshot/Mathmate-7B-DELLA-ORPO-C

SGLang

How to use Haleshot/Mathmate-7B-DELLA-ORPO-C 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 "Haleshot/Mathmate-7B-DELLA-ORPO-C" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haleshot/Mathmate-7B-DELLA-ORPO-C",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Haleshot/Mathmate-7B-DELLA-ORPO-C" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haleshot/Mathmate-7B-DELLA-ORPO-C",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Haleshot/Mathmate-7B-DELLA-ORPO-C with Docker Model Runner:
```
docker model run hf.co/Haleshot/Mathmate-7B-DELLA-ORPO-C
```

Mathmate-7B-DELLA-ORPO-C

Mathmate-7B-DELLA-ORPO-C is a LoRA adapter for Haleshot/Mathmate-7B-DELLA-ORPO, finetuned to improve performance on everyday conversations.

Model Details

Base Model: Haleshot/Mathmate-7B-DELLA
Training Dataset: HuggingFaceTB/everyday-conversations-llama3.1-2k

Dataset

The model was finetuned on the HuggingFaceTB/everyday-conversations-llama3.1-2k dataset, which focuses on everyday conversations and small talk.

Usage

To use this LoRA adapter, you need to load both the base model and the adapter. Here's an example:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import torch

base_model_name = "Haleshot/Mathmate-7B-DELLA"
adapter_name = "Haleshot/Mathmate-7B-DELLA-ORPO-C"

base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(base_model, adapter_name)

def generate_response(prompt, max_length=512):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1, do_sample=True, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

prompt = "Let's have a casual conversation about the weather today."
response = generate_response(prompt)
print(response)

Acknowledgements

Thanks to the HuggingFaceTB team for providing the everyday conversations dataset used in this finetuning process.

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Model tree for Haleshot/Mathmate-7B-DELLA-ORPO-C

Base model

Haleshot/Mathmate-7B-DELLA

Finetuned

Haleshot/Mathmate-7B-DELLA-ORPO