Text Generation
Transformers
PyTorch
English
custom-architecture
rope
rmsnorm
swiglu
flash-attention
16k-context
Eval Results (legacy)
Instructions to use Austin207/Map-NEO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Austin207/Map-NEO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Austin207/Map-NEO")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Austin207/Map-NEO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Austin207/Map-NEO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Austin207/Map-NEO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Austin207/Map-NEO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Austin207/Map-NEO
- SGLang
How to use Austin207/Map-NEO 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 "Austin207/Map-NEO" \ --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": "Austin207/Map-NEO", "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 "Austin207/Map-NEO" \ --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": "Austin207/Map-NEO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Austin207/Map-NEO with Docker Model Runner:
docker model run hf.co/Austin207/Map-NEO
| """debug_downloaded_data.py - Inspect the downloaded conversation format""" | |
| import json | |
| def inspect_downloaded_data(): | |
| """Inspect the first few records to understand the format""" | |
| data_path = "data/conversation_raw/OpenAssistant_oasst1_raw.jsonl" | |
| print("🔍 Inspecting downloaded OpenAssistant data...") | |
| print("="*50) | |
| try: | |
| with open(data_path, 'r', encoding='utf-8') as f: | |
| for i in range(5): | |
| line = f.readline().strip() | |
| if line: | |
| record = json.loads(line) | |
| print(f"\nRecord {i+1}:") | |
| print(f"Top-level keys: {list(record.keys())}") | |
| # Show sample content for each key | |
| for key, value in record.items(): | |
| if isinstance(value, str) and len(value) > 100: | |
| value = value[:100] + "..." | |
| elif isinstance(value, dict): | |
| value = f"Dict with keys: {list(value.keys())}" | |
| elif isinstance(value, list): | |
| value = f"List with {len(value)} items" | |
| print(f" {key}: {value}") | |
| # If there's a nested structure, explore it | |
| for key in ['prompt', 'conversation', 'messages']: | |
| if key in record and isinstance(record[key], (dict, list)): | |
| print(f"\n Exploring {key}:") | |
| nested = record[key] | |
| if isinstance(nested, dict): | |
| print(f" Keys: {list(nested.keys())}") | |
| for nkey, nvalue in list(nested.items())[:3]: | |
| if isinstance(nvalue, str) and len(nvalue) > 50: | |
| nvalue = nvalue[:50] + "..." | |
| print(f" {nkey}: {nvalue}") | |
| elif isinstance(nested, list) and nested: | |
| print(f" First item type: {type(nested[0])}") | |
| if isinstance(nested, dict): | |
| print(f" First item keys: {list(nested.keys())}") | |
| except Exception as e: | |
| print(f"Error reading file: {e}") | |
| if __name__ == "__main__": | |
| inspect_downloaded_data() | |