Instructions to use jordiclive/scaled-llama-7b-lora-16k-rp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jordiclive/scaled-llama-7b-lora-16k-rp2 with Transformers:

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

pipe = pipeline("text-generation", model="jordiclive/scaled-llama-7b-lora-16k-rp2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jordiclive/scaled-llama-7b-lora-16k-rp2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jordiclive/scaled-llama-7b-lora-16k-rp2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jordiclive/scaled-llama-7b-lora-16k-rp2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jordiclive/scaled-llama-7b-lora-16k-rp2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jordiclive/scaled-llama-7b-lora-16k-rp2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jordiclive/scaled-llama-7b-lora-16k-rp2

SGLang

How to use jordiclive/scaled-llama-7b-lora-16k-rp2 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 "jordiclive/scaled-llama-7b-lora-16k-rp2" \
    --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": "jordiclive/scaled-llama-7b-lora-16k-rp2",
		"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 "jordiclive/scaled-llama-7b-lora-16k-rp2" \
        --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": "jordiclive/scaled-llama-7b-lora-16k-rp2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jordiclive/scaled-llama-7b-lora-16k-rp2 with Docker Model Runner:
```
docker model run hf.co/jordiclive/scaled-llama-7b-lora-16k-rp2
```

Linear Scaled RoPE LLama LoRA 16k

import torch
from transformers import LlamaTokenizerFast, AutoModelForCausalLM

model_name = "jordiclive/scaled-llama-7b-lora-16k-rp2"


model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,trust_remote_code=True
)
tokenizer = LlamaTokenizerFast.from_pretrained(
        model_name)


tokenizer.model_max_length = 16384
tokenizer.pad_token = tokenizer.eos_token
model.max_sequence_length = tokenizer.model_max_length

huggyllama/llama-7b Trained on Packed 16k sequences of the RedPajama dataset for 1 Epoch.
Merged Model. If require LoRA parameters/config, they are in the adapter folder.

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