Instructions to use collision-10M/Collision-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Collision-1B / tokenizer.json

collision-10M

Release collision-10M/Collision-1B with In-House NLP Engine & Grounded Intelligence

1653631 verified 17 days ago

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1.14 kB

	{
	"version": "1.0",
	"truncation": null,
	"padding": null,
	"added_tokens": [
	{
	"id": 256,
	"content": "[PAD]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	},
	{
	"id": 257,
	"content": "[UNK]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	},
	{
	"id": 258,
	"content": "[BOS]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	},
	{
	"id": 259,
	"content": "[EOS]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	}
	],
	"normalizer": null,
	"pre_tokenizer": null,
	"post_processor": null,
	"decoder": null,
	"model": {
	"type": "BPE",
	"dropout": null,
	"unk_token": "[UNK]",
	"continuing_subword_prefix": null,
	"end_of_word_suffix": null,
	"fuse_unk": false,
	"byte_fallback": false,
	"vocab": {},
	"merges": []
	}
	}