Text Generation
Transformers
PyTorch
Safetensors
GGUF
English
collision
conversational
rag
reasoning
deepseek-r1-style
system-2
agent
ollama
llama.cpp
fastapi
openai-compatible
slm
edge-ai
cpu-first
in-house-nlp
math
keyphrase-extraction
topic-classification
grammar-correction
reading-comprehension
sentiment-analysis
research
educational
custom_code
Instructions to use collision-10M/Collision-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use collision-10M/Collision-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="collision-10M/Collision-1B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("collision-10M/Collision-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use collision-10M/Collision-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "collision-10M/Collision-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "collision-10M/Collision-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/collision-10M/Collision-1B
- SGLang
How to use collision-10M/Collision-1B 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 "collision-10M/Collision-1B" \ --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": "collision-10M/Collision-1B", "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 "collision-10M/Collision-1B" \ --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": "collision-10M/Collision-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use collision-10M/Collision-1B with Docker Model Runner:
docker model run hf.co/collision-10M/Collision-1B
File size: 1,188 Bytes
ddc12cc | 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 | import os
import sys
def main():
print("Setting up COLLISION-1M project structure...")
# Required directories
dirs = [
"data/raw",
"data/processed",
"data/samples",
"checkpoints",
"experiments",
"artifacts/tokenizer",
"configs"
]
for d in dirs:
os.makedirs(d, exist_ok=True)
print(f"Directory verified/created: {d}")
# Generate sample raw dataset if not present
sample_path = os.path.join("data", "raw", "sample.txt")
if not os.path.exists(sample_path):
from data.prepare import generate_sample_text
with open(sample_path, "w", encoding="utf-8") as f:
f.write(generate_sample_text())
print(f"Created demo raw dataset at {sample_path}")
# Check dependencies
try:
import torch
import numpy
print(f"PyTorch version: {torch.__version__}")
print(f"NumPy version: {numpy.__version__}")
except ImportError as e:
print(f"Warning: Missing dependency: {e}. Please run 'pip install -r requirements.txt'")
print("\nCOLLISION-1M initialized successfully.")
if __name__ == "__main__":
main()
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