Instructions to use akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2 with Transformers:

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

pipe = pipeline("text-generation", model="akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2")
model = AutoModelForCausalLM.from_pretrained("akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2")
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

vLLM

How to use akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2

SGLang

How to use akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2 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 "akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2" \
    --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": "akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2",
		"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 "akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2" \
        --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": "akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2 with Docker Model Runner:
```
docker model run hf.co/akankshanc7/tiny-aya-global-em-code-en-code-insecure-seed_2
```

tiny-aya-global-em-code-en-code-insecure-seed_2

File size: 681 Bytes

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{
  "add_prefix_space": false,
  "backend": "tokenizers",
  "bos_token": "<BOS_TOKEN>",
  "clean_up_tokenization_spaces": false,
  "cls_token": "<CLS>",
  "eos_token": "<|END_OF_TURN_TOKEN|>",
  "errors": "replace",
  "extra_special_tokens": [
    "<|START_RESPONSE|>",
    "<|END_RESPONSE|>"
  ],
  "is_local": false,
  "legacy": true,
  "mask_token": "<MASK_TOKEN>",
  "model_max_length": 1000000000000000019884624838656,
  "model_specific_special_tokens": {},
  "pad_token": "<PAD>",
  "sep_token": "<SEP>",
  "sp_model_kwargs": {},
  "spaces_between_special_tokens": false,
  "tokenizer_class": "CohereTokenizer",
  "unk_token": "<UNK>",
  "use_default_system_prompt": false
}