Instructions to use TareksGraveyard/DMHC-A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksGraveyard/DMHC-A with Transformers:

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

pipe = pipeline("text-generation", model="TareksGraveyard/DMHC-A")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TareksGraveyard/DMHC-A")
model = AutoModelForCausalLM.from_pretrained("TareksGraveyard/DMHC-A")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TareksGraveyard/DMHC-A with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TareksGraveyard/DMHC-A

SGLang

How to use TareksGraveyard/DMHC-A 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 "TareksGraveyard/DMHC-A" \
    --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": "TareksGraveyard/DMHC-A",
		"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 "TareksGraveyard/DMHC-A" \
        --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": "TareksGraveyard/DMHC-A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksGraveyard/DMHC-A with Docker Model Runner:
```
docker model run hf.co/TareksGraveyard/DMHC-A
```

merge

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

Merge Details

Merge Method

This model was merged using the Linear DELLA merge method using VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct as a base.

Models Merged

The following models were included in the merge:

Sao10K/L3.3-70B-Euryale-v2.3

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Sao10K/L3.3-70B-Euryale-v2.3
    parameters:
      weight: 0.5
      density: 0.7
      epsilon: 0.2
      lambda: 1.1
  - model: VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
    parameters:
      weight: 0.5
      density: 0.7
      epsilon: 0.1
      lambda: 1
base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
merge_method: della_linear
parameters:
  normalize: false
  int8_mask: true
chat_template: llama3
tokenizer:
  source: union
dtype: float32
out_dtype: bfloat16

Downloads last month: 2

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for TareksGraveyard/DMHC-A

Sao10K/L3.3-70B-Euryale-v2.3

VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct

Merge model

this model

Paper for TareksGraveyard/DMHC-A

DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling

Paper • 2406.11617 • Published Jun 17, 2024 • 10