Instructions to use lifeofcoding/mastermax-llama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lifeofcoding/mastermax-llama-7b with Transformers:

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

pipe = pipeline("text-generation", model="lifeofcoding/mastermax-llama-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lifeofcoding/mastermax-llama-7b")
model = AutoModelForCausalLM.from_pretrained("lifeofcoding/mastermax-llama-7b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lifeofcoding/mastermax-llama-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lifeofcoding/mastermax-llama-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lifeofcoding/mastermax-llama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lifeofcoding/mastermax-llama-7b

SGLang

How to use lifeofcoding/mastermax-llama-7b 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 "lifeofcoding/mastermax-llama-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lifeofcoding/mastermax-llama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "lifeofcoding/mastermax-llama-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lifeofcoding/mastermax-llama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lifeofcoding/mastermax-llama-7b with Docker Model Runner:
```
docker model run hf.co/lifeofcoding/mastermax-llama-7b
```

Mastermax Llama 7B

This is a a Llama2 7B base model that was fined tuned on additional datasets, in attempts improve performance.

How to use with HugginFace pipeline

from transformers import AutoModelForCausalLM, AutoTokenizer, pineline

model = AutoModelForCausalLM.from_pretrained(
          "lifeofcoding/mastermax-llama-7b",
          load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("lifeofcoding/mastermax-llama-7b", trust_remote_code=True)

# Generate text using the pipeline
pipe = pipeline(task="text-generation",
                model=model,
                tokenizer=tokenizer,
                max_length=200)

result = pipe(f"<s>[INST] {prompt} [/INST]")
generated_text = result[0]['generated_text']

Developed by: LifeOfCoding
Finetuned from model: GuardRail Llama2 7B Sharded

Downloads last month: 4

lifeofcoding
/

mastermax-llama-7b

Mastermax Llama 7B

How to use with HugginFace pipeline

Datasets used to train lifeofcoding/mastermax-llama-7b