Instructions to use bfuzzy1/acheron-m1a-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bfuzzy1/acheron-m1a-llama with Transformers:

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

pipe = pipeline("text-generation", model="bfuzzy1/acheron-m1a-llama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bfuzzy1/acheron-m1a-llama", dtype="auto")

PEFT
How to use bfuzzy1/acheron-m1a-llama with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bfuzzy1/acheron-m1a-llama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bfuzzy1/acheron-m1a-llama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bfuzzy1/acheron-m1a-llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bfuzzy1/acheron-m1a-llama

SGLang

How to use bfuzzy1/acheron-m1a-llama 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 "bfuzzy1/acheron-m1a-llama" \
    --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": "bfuzzy1/acheron-m1a-llama",
		"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 "bfuzzy1/acheron-m1a-llama" \
        --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": "bfuzzy1/acheron-m1a-llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bfuzzy1/acheron-m1a-llama with Docker Model Runner:
```
docker model run hf.co/bfuzzy1/acheron-m1a-llama
```

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "bfuzzy1/acheron-m1a-llama"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto',
    trust_remote_code=True
)

messages = [
    {"role": "user", "content": "What's 2 + 2 -3?"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(
    input_ids.to('mps' if torch.backends.mps.is_available() else 'cpu'),
    max_new_tokens=100
)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

print(response)

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2 items • Updated Jan 11, 2025