Instructions to use jeffmeloy/Qwen2.5-7B-minperplexity-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeffmeloy/Qwen2.5-7B-minperplexity-1 with Transformers:

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

pipe = pipeline("text-generation", model="jeffmeloy/Qwen2.5-7B-minperplexity-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jeffmeloy/Qwen2.5-7B-minperplexity-1")
model = AutoModelForCausalLM.from_pretrained("jeffmeloy/Qwen2.5-7B-minperplexity-1", 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 jeffmeloy/Qwen2.5-7B-minperplexity-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeffmeloy/Qwen2.5-7B-minperplexity-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeffmeloy/Qwen2.5-7B-minperplexity-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeffmeloy/Qwen2.5-7B-minperplexity-1

SGLang

How to use jeffmeloy/Qwen2.5-7B-minperplexity-1 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 "jeffmeloy/Qwen2.5-7B-minperplexity-1" \
    --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": "jeffmeloy/Qwen2.5-7B-minperplexity-1",
		"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 "jeffmeloy/Qwen2.5-7B-minperplexity-1" \
        --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": "jeffmeloy/Qwen2.5-7B-minperplexity-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jeffmeloy/Qwen2.5-7B-minperplexity-1 with Docker Model Runner:
```
docker model run hf.co/jeffmeloy/Qwen2.5-7B-minperplexity-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Description

Model created by analyzing and selecting the lowest perplexity for each layer from other Qwen2.5-7B models.

"edgerunner-ai/EdgeRunner-Command-Nested"
"EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1"
"fblgit/cybertron-v4-qw7B-MGS"
"FourOhFour/Vapor_v2_7B"
"Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2"
"happzy2633/qwen2.5-7b-ins-v3"
"huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2"
"HumanLLMs/Humanish-Qwen2.5-7B-Instruct"
"katanemo/Arch-Function-7B"
"Orion-zhen/Meissa-Qwen2.5-7B-Instruct"
"Orion-zhen/Qwen2.5-7B-Gutenberg-KTO"
"Orion-zhen/Qwen2.5-7B-Instruct-Uncensored"
"newsbang/Homer-7B-v0.1"
"nhyha/merge_Qwen2.5-7B-Instruct_20241023_0314"
"Qwen/Qwen2.5-7B"
"Qwen/Qwen2.5-7B-Instruct"
"scilo/qwen_ft_no_temp"
"sethuiyer/Qwen2.5-7B-Anvita"
"Siheng99/Qwen2.5-7B-Instruct-SEALONG"
"thomas-yanxin/XinYuan-Qwen2.5-7B-0917"
"newsbang/Homer-v0.5-Qwen2.5-7B"
"fblgit/cybertron-v4-qw7B-UNAMGS"
"win10/Verdandi-Qwen2.5-7B"
"AmberYifan/Qwen2.5-7B-dpo-2k"
"nextvalueup/Qwen2.5-7B-Instruct_v3"
"jbjeong91/Qwen2.5_7B_IST_StoryGen_vanilla"
"AmberYifan/Qwen2.5-7B-dpo-2k-hhrlhf"
"jbjeong91/Qwen2.5_7B_IST_StoryGen_test2"
"ZeroXClem/Qwen2.5-7B-HomerCreative-Mix"
"bunnycore/Qandora-2.5-7B"

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Safetensors

Model size

8B params

Tensor type

BF16

F32

Model tree for jeffmeloy/Qwen2.5-7B-minperplexity-1

Base model

Qwen/Qwen2.5-7B

Finetuned

(954)

this model

Quantizations

3 models