Instructions to use sequelbox/Llama2-70B-SpellBlade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sequelbox/Llama2-70B-SpellBlade with Transformers:

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

pipe = pipeline("text-generation", model="sequelbox/Llama2-70B-SpellBlade")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sequelbox/Llama2-70B-SpellBlade")
model = AutoModelForCausalLM.from_pretrained("sequelbox/Llama2-70B-SpellBlade")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sequelbox/Llama2-70B-SpellBlade with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sequelbox/Llama2-70B-SpellBlade"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sequelbox/Llama2-70B-SpellBlade",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sequelbox/Llama2-70B-SpellBlade

SGLang

How to use sequelbox/Llama2-70B-SpellBlade 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 "sequelbox/Llama2-70B-SpellBlade" \
    --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": "sequelbox/Llama2-70B-SpellBlade",
		"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 "sequelbox/Llama2-70B-SpellBlade" \
        --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": "sequelbox/Llama2-70B-SpellBlade",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sequelbox/Llama2-70B-SpellBlade with Docker Model Runner:
```
docker model run hf.co/sequelbox/Llama2-70B-SpellBlade
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spell Blade is a chat and general capability finetuned upgrade to Llama 2, focused on improving conversational quality as well as supplementing technical capability.

Performs solidly as-is, user satisfaction will be optimized with further finetuning.

Most training data utilizes the [INST][/INST] chat format.

This is a 'legacy model' offered primarily for reference purposes. I recommend Llama 3 over this model for general use.

Downloads last month: 72

Safetensors

Model size

69B params

Tensor type

F32

Model tree for sequelbox/Llama2-70B-SpellBlade

Quantizations

3 models