Instructions to use lilmeaty/model-7v08feylu8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lilmeaty/model-7v08feylu8 with Transformers:

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

pipe = pipeline("text-generation", model="lilmeaty/model-7v08feylu8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lilmeaty/model-7v08feylu8")
model = AutoModelForCausalLM.from_pretrained("lilmeaty/model-7v08feylu8")
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

vLLM

How to use lilmeaty/model-7v08feylu8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lilmeaty/model-7v08feylu8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lilmeaty/model-7v08feylu8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lilmeaty/model-7v08feylu8

SGLang

How to use lilmeaty/model-7v08feylu8 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 "lilmeaty/model-7v08feylu8" \
    --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": "lilmeaty/model-7v08feylu8",
		"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 "lilmeaty/model-7v08feylu8" \
        --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": "lilmeaty/model-7v08feylu8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lilmeaty/model-7v08feylu8 with Docker Model Runner:
```
docker model run hf.co/lilmeaty/model-7v08feylu8
```

    license: apache-2.0
    base_model:
    tags:
    - merge
    - mergekit
    - lazymergekit
    ---
    # lilmeaty/model-7v08feylu8
    lilmeaty/model-7v08feylu8 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
    ## 🧩 Configuration
    yamlmodels:

model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw
model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw merge_method: slerp base_model: Hjgugugjhuhjggg/mergekit-passthrough-jzygynw dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0]

💻 Usage

    python
    !pip install -qU transformers accelerate
    from transformers import AutoTokenizer
    import transformers
    import torch
    model = "lilmeaty/lilmeaty/model-7v08feylu8"
    messages = [{"role": "user", "content": "What is a large language model?"}]
    tokenizer = AutoTokenizer.from_pretrained(model)
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        torch_dtype=torch.float16,
        device_map="auto",
    )
    outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
    print(outputs[0]["generated_text"])

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Safetensors

Model size

29B params

Tensor type

BF16