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Instructions to use icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2")
model = AutoModelForCausalLM.from_pretrained("icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2

SGLang

How to use icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2 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 "icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2" \
    --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": "icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2",
		"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 "icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2" \
        --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": "icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

IceLemonMedovukhaRP-7b-4.2bpw-exl2

Base model

Link on my new AI related discord server for feedback, questions and other stuff.

ko-fi To buy sweets for my cat :3

Download

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called IceLemonMedovukhaRP-7b-4.2bpw-exl2:

mkdir IceLemonMedovukhaRP-7b-4.2bpw-exl2
huggingface-cli download icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2 --local-dir IceLemonMedovukhaRP-7b-4.2bpw-exl2 --local-dir-use-symlinks False

More advanced huggingface-cli download usage

If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.

The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

mkdir FOLDERNAME
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MODEL --local-dir FOLDERNAME --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Downloads last month: 10

Model tree for icefog72/IceLemonMedovukhaRP-7b-4.2bpw-exl2

Base model

icefog72/IceMedovukhaRP-7b

Finetuned

icefog72/Ice0.101-20.03-RP-GRPO-1

Finetuned

icefog72/IceLemonMedovukhaRP-7b

Quantized

(9)

this model