Text Generation
PEFT
Safetensors
Transformers
English
llama
lora
dpo
smollm2
trl
conversational
text-generation-inference
Instructions to use Subject-Emu-5259/NeuralAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Subject-Emu-5259/NeuralAI with PEFT:
Base model is not found.
- Transformers
How to use Subject-Emu-5259/NeuralAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Subject-Emu-5259/NeuralAI", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Subject-Emu-5259/NeuralAI") model = AutoModelForCausalLM.from_pretrained("Subject-Emu-5259/NeuralAI", 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 Subject-Emu-5259/NeuralAI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Subject-Emu-5259/NeuralAI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Subject-Emu-5259/NeuralAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Subject-Emu-5259/NeuralAI
- SGLang
How to use Subject-Emu-5259/NeuralAI 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 "Subject-Emu-5259/NeuralAI" \ --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": "Subject-Emu-5259/NeuralAI", "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 "Subject-Emu-5259/NeuralAI" \ --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": "Subject-Emu-5259/NeuralAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Subject-Emu-5259/NeuralAI with Docker Model Runner:
docker model run hf.co/Subject-Emu-5259/NeuralAI
File size: 3,124 Bytes
38b4eff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | #!/usr/bin/env python3
"""
NeuralAI β Hugging Face Pull Script
Downloads model checkpoints, training data, and configs from the Hub.
"""
import os
import sys
from huggingface_hub import HfApi, snapshot_download
REPO_ID = "Subject-Emu-5259/NeuralAI"
LOCAL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def main():
api = HfApi()
user = api.whoami(token=True)
print(f"π Connected as: {user['name']}")
print(f"π¦ Repo: {REPO_ID}")
action = sys.argv[1] if len(sys.argv) > 1 else "all"
if action == "model" or action == "all":
print("\nπ₯ Pulling model adapter...")
model_files = [
"adapter_config.json",
"adapter_model.safetensors",
"chat_template.jinja",
"tokenizer.json",
"tokenizer_config.json",
"training_log.json",
]
for f in model_files:
try:
api.hf_hub_download(
repo_id=REPO_ID,
filename=f,
local_dir=os.path.join(LOCAL_DIR, "checkpoints", "v2_model"),
local_dir_use_symlinks=False,
token=True
)
print(f" β
{f}")
except Exception as e:
print(f" βοΈ {f} (not found on Hub)")
if action == "data" or action == "all":
print("\nπ₯ Pulling training data...")
try:
files = api.list_repo_files(REPO_ID, repo_type="model", token=True)
data_files = [f for f in files if f.startswith("data/")]
for f in data_files:
local_path = os.path.join(LOCAL_DIR, f)
os.makedirs(os.path.dirname(local_path), exist_ok=True)
api.hf_hub_download(
repo_id=REPO_ID,
filename=f,
local_dir=LOCAL_DIR,
local_dir_use_symlinks=False,
token=True
)
print(f" β
{f}")
except Exception as e:
print(f" β Error: {e}")
if action == "scripts" or action == "all":
print("\nπ₯ Pulling training scripts...")
try:
files = api.list_repo_files(REPO_ID, repo_type="model", token=True)
for prefix in ["training/", "services/", "tools/"]:
for f in [x for x in files if x.startswith(prefix)]:
local_path = os.path.join(LOCAL_DIR, f)
os.makedirs(os.path.dirname(local_path), exist_ok=True)
api.hf_hub_download(
repo_id=REPO_ID,
filename=f,
local_dir=LOCAL_DIR,
local_dir_use_symlinks=False,
token=True
)
print(f" β
{f}")
except Exception as e:
print(f" β Error: {e}")
print(f"\n{'='*50}")
print(f"β
Pull complete! Files synced to {LOCAL_DIR}")
print(f"{'='*50}")
if __name__ == "__main__":
main()
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