Instructions to use TheNormsOfIntelligence/ATC_Nima_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheNormsOfIntelligence/ATC_Nima_Model with Transformers:

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

pipe = pipeline("text-generation", model="TheNormsOfIntelligence/ATC_Nima_Model")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheNormsOfIntelligence/ATC_Nima_Model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheNormsOfIntelligence/ATC_Nima_Model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheNormsOfIntelligence/ATC_Nima_Model

SGLang

How to use TheNormsOfIntelligence/ATC_Nima_Model 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 "TheNormsOfIntelligence/ATC_Nima_Model" \
    --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": "TheNormsOfIntelligence/ATC_Nima_Model",
		"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 "TheNormsOfIntelligence/ATC_Nima_Model" \
        --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": "TheNormsOfIntelligence/ATC_Nima_Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheNormsOfIntelligence/ATC_Nima_Model with Docker Model Runner:
```
docker model run hf.co/TheNormsOfIntelligence/ATC_Nima_Model
```

ATC_Nima_Model / tokenizer_config.json

TheNormsOfIntelligence

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	{
	"add_bos_token": false,
	"add_eos_token": false,
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"199999": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"200018": {
	"content": "<\|endofprompt\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"200019": {
	"content": "<\|assistant\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": true
	},
	"200020": {
	"content": "<\|end\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": true
	},
	"200021": {
	"content": "<\|user\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": true
	},
	"200022": {
	"content": "<\|system\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": true
	},
	"200023": {
	"content": "<\|tool\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": false
	},
	"200024": {
	"content": "<\|/tool\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": false
	},
	"200025": {
	"content": "<\|tool_call\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": false
	},
	"200026": {
	"content": "<\|/tool_call\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": false
	},
	"200027": {
	"content": "<\|tool_response\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": false
	},
	"200028": {
	"content": "<\|tag\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": true,
	"single_word": false,
	"special": true
	}
	},
	"bos_token": "<\|endoftext\|>",
	"chat_template": "{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<\|' + message['role'] + '\|>' + message['content'] + '<\|tool\|>' + message['tools'] + '<\|/tool\|>' + '<\|end\|>' }}{% else %}{{ '<\|' + message['role'] + '\|>' + message['content'] + '<\|end\|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<\|assistant\|>' }}{% else %}{{ eos_token }}{% endif %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|endoftext\|>",
	"model_max_length": 131072,
	"pad_token": "<\|endoftext\|>",
	"tokenizer_class": "GPT2Tokenizer",
	"unk_token": "<\|endoftext\|>"
	}