Instructions to use TareksGraveyard/Savant-Qwen2.5-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksGraveyard/Savant-Qwen2.5-72B with Transformers:

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

pipe = pipeline("text-generation", model="TareksGraveyard/Savant-Qwen2.5-72B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TareksGraveyard/Savant-Qwen2.5-72B")
model = AutoModelForCausalLM.from_pretrained("TareksGraveyard/Savant-Qwen2.5-72B", device_map="auto")
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/Savant-Qwen2.5-72B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TareksGraveyard/Savant-Qwen2.5-72B"
# 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/Savant-Qwen2.5-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TareksGraveyard/Savant-Qwen2.5-72B

SGLang

How to use TareksGraveyard/Savant-Qwen2.5-72B 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/Savant-Qwen2.5-72B" \
    --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/Savant-Qwen2.5-72B",
		"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/Savant-Qwen2.5-72B" \
        --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/Savant-Qwen2.5-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksGraveyard/Savant-Qwen2.5-72B with Docker Model Runner:
```
docker model run hf.co/TareksGraveyard/Savant-Qwen2.5-72B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

After some success with my merging my favorite Llama 3 models, I decided to try my hand on some Qwen 2.5 models I have tried and enjoyed. I never quite got fully onto the Qwen bandwagon as I always preferred LLaMa, but a lot of folks swear by Qwen. In my limited experience with Qwen I have enjoyed these models and merged something decent I think. For this merge I went for an default parameter Della method.

merge

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

Merge Details

Merge Method

This model was merged using the della_linear merge method using zetasepic/Qwen2.5-72B-Instruct-abliterated as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: spow12/ChatWaifu_72B_v2.2
    parameters:
      weight: 0.25
  - model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2
    parameters:
      weight: 0.25
  - model: Steelskull/Q2.5-MS-Mistoria-72b-v2
    parameters:
      weight: 0.25
  - model: Sao10K/72B-Qwen2.5-Kunou-v1
    parameters:
      weight: 0.25
merge_method: della_linear
base_model: zetasepic/Qwen2.5-72B-Instruct-abliterated
dtype: bfloat16
tokenizer_source: union

Downloads last month: 10

Safetensors

Model size

73B params

Tensor type

BF16

Model tree for TareksGraveyard/Savant-Qwen2.5-72B

EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2

Sao10K/72B-Qwen2.5-Kunou-v1

SteelStorage/Q2.5-MS-Mistoria-72b-v2

spow12/ChatWaifu_72B_v2.2

zetasepic/Qwen2.5-72B-Instruct-abliterated

Merge model

this model

Quantizations

2 models