Instructions to use EpistemeAI/Fireball-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI/Fireball-12B with Transformers:

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

pipe = pipeline("text-generation", model="EpistemeAI/Fireball-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-12B")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-12B", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EpistemeAI/Fireball-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EpistemeAI/Fireball-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/Fireball-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EpistemeAI/Fireball-12B

SGLang

How to use EpistemeAI/Fireball-12B 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 "EpistemeAI/Fireball-12B" \
    --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": "EpistemeAI/Fireball-12B",
		"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 "EpistemeAI/Fireball-12B" \
        --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": "EpistemeAI/Fireball-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use EpistemeAI/Fireball-12B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI/Fireball-12B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI/Fireball-12B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EpistemeAI/Fireball-12B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EpistemeAI/Fireball-12B",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI/Fireball-12B with Docker Model Runner:
```
docker model run hf.co/EpistemeAI/Fireball-12B
```

legolasyiu commited on Aug 26, 2024

Commit

eb2798f

verified ·

1 Parent(s): a61e766

Update README.md

Browse files

Files changed (1) hide show

README.md +16 -0

README.md CHANGED Viewed

@@ -82,6 +82,22 @@ Mistral Nemo is a transformer model, with the following architecture choices:
 For guardrailing and moderating prompts against indirect/direct prompt injections and jailbreaking, please follow the SentinelShield AI GitHub repository:
 [SentinelShield AI](https://github.com/tomtyiu/SentinelShieldAI)
 #### Demo

 For guardrailing and moderating prompts against indirect/direct prompt injections and jailbreaking, please follow the SentinelShield AI GitHub repository:
 [SentinelShield AI](https://github.com/tomtyiu/SentinelShieldAI)
+## Prompt Template: Alpaca (recommended)
+plesee use Alpaca prompt
+```python
+f"""Below is an instruction that describes a task. \
+    Write a response that appropriately completes the request.
+    ### Instruction:
+    {x['instruction']}
+    ### Input:
+    {x['input']}
+    ### Response:
+    """
+```
 #### Demo