Instructions to use NeuronUz/NeuronAI-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeuronUz/NeuronAI-4B with Transformers:

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

pipe = pipeline("text-generation", model="NeuronUz/NeuronAI-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeuronUz/NeuronAI-4B")
model = AutoModelForCausalLM.from_pretrained("NeuronUz/NeuronAI-4B", 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 NeuronUz/NeuronAI-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeuronUz/NeuronAI-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuronUz/NeuronAI-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeuronUz/NeuronAI-4B

SGLang

How to use NeuronUz/NeuronAI-4B 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 "NeuronUz/NeuronAI-4B" \
    --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": "NeuronUz/NeuronAI-4B",
		"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 "NeuronUz/NeuronAI-4B" \
        --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": "NeuronUz/NeuronAI-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NeuronUz/NeuronAI-4B with Docker Model Runner:
```
docker model run hf.co/NeuronUz/NeuronAI-4B
```

kmamaroziqov commited on 9 days ago

Commit

d3d2265

verified ·

1 Parent(s): de2c622

Remove checkpoint selection section

Browse files

Files changed (1) hide show

README.md +0 -15

README.md CHANGED Viewed

@@ -158,21 +158,6 @@ python benchmark.py --tasks flores --limit 200 --output flores.json
 above; 200-example quick runs are sanity checks. COMET downloads the
 `Unbabel/wmt22-comet-da` evaluator and needs additional disk/RAM.
-## Why this checkpoint
-Four retained checkpoints were run through the complete suite. Checkpoint 4749
-(one SFT epoch) produced the best downstream score even though development loss
-was fractionally lower at checkpoint 3564.
-![Checkpoint selection](assets/checkpoint_selection.png)
-| Checkpoint | Epoch | Dev loss | Normalized score |
-| ---: | ---: | ---: | ---: |
-| 1188 | 0.25 | 1.4316 | 0.6643 |
-| 2376 | 0.50 | 1.3910 | 0.6600 |
-| 3564 | 0.75 | **1.3669** | 0.6609 |
-| **4749** | **1.00** | 1.3675 | **0.6724** |
 ## Uzbek tokenizer efficiency
 The tokenizer is an in-place, primarily **Latin-script Uzbek** retrofit rather

 above; 200-example quick runs are sanity checks. COMET downloads the
 `Unbabel/wmt22-comet-da` evaluator and needs additional disk/RAM.
 ## Uzbek tokenizer efficiency
 The tokenizer is an in-place, primarily **Latin-script Uzbek** retrofit rather