Instructions to use Finnish-NLP/Ahma-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finnish-NLP/Ahma-3B with Transformers:

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

pipe = pipeline("text-generation", model="Finnish-NLP/Ahma-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Finnish-NLP/Ahma-3B")
model = AutoModelForCausalLM.from_pretrained("Finnish-NLP/Ahma-3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Finnish-NLP/Ahma-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Finnish-NLP/Ahma-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finnish-NLP/Ahma-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Finnish-NLP/Ahma-3B

SGLang

How to use Finnish-NLP/Ahma-3B 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 "Finnish-NLP/Ahma-3B" \
    --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": "Finnish-NLP/Ahma-3B",
		"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 "Finnish-NLP/Ahma-3B" \
        --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": "Finnish-NLP/Ahma-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Finnish-NLP/Ahma-3B with Docker Model Runner:
```
docker model run hf.co/Finnish-NLP/Ahma-3B
```

Ahma-3B / EasyLM /scripts /lm_eval_json.py

aapot

Add easylm training code

5a63fc6 over 2 years ago

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1.66 kB

	import json
	import mlxu
	from EasyLM.serving import LMClient


	FLAGS, FLAGS_DEF = mlxu.define_flags_with_default(
	input_file='',
	output_file='',
	prefix_field='prefix',
	text_field='text',
	until_field='until',
	eval_type='loglikelihood',
	lm_client=LMClient.get_default_config(),
	)


	def main(argv):
	lm_client = LMClient(FLAGS.lm_client)
	with mlxu.open_file(FLAGS.input_file, 'r') as fin:
	input_data = json.load(fin)

	if FLAGS.eval_type == 'loglikelihood':
	prefix = input_data[FLAGS.prefix_field]
	text = input_data[FLAGS.text_field]
	loglikelihoods, is_greedys = lm_client.loglikelihood(prefix, text)
	output_data = {
	'loglikelihood': loglikelihoods,
	'is_greedy': is_greedys,
	}
	elif FLAGS.eval_type == 'loglikelihood_rolling':
	text = input_data[FLAGS.text_field]
	loglikelihoods, is_greedys = lm_client.loglikelihood_rolling(text)
	output_data = {
	'loglikelihood': loglikelihoods,
	'is_greedy': is_greedys,
	}
	elif FLAGS.eval_type == 'greedy_until':
	prefix = input_data[FLAGS.prefix_field]
	until = input_data[FLAGS.until_field]
	output_data = {'output_text': lm_client.greedy_until(prefix, until)}
	elif FLAGS.eval_type == 'generate':
	prefix = input_data[FLAGS.prefix_field]
	output_data = {'output_text': lm_client.generate(prefix)}
	else:
	raise ValueError(f'Unknown eval_type: {FLAGS.eval_type}')

	with mlxu.open_file(FLAGS.output_file, 'w') as fout:
	json.dump(output_data, fout)


	if __name__ == "__main__":
	mlxu.run(main)