Instructions to use OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated with Transformers:

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

pipe = pipeline("text-generation", model="OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated

SGLang

How to use OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated 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 "OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated" \
    --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": "OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated",
		"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 "OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated" \
        --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": "OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated with Docker Model Runner:
```
docker model run hf.co/OpenIntelligenceNet/Spark-X2.6-1.7B-Abliterated
```

Spark-X2.6-1.7B-Abliterated / generation_config.json

OpenIntelligenceNet

Fix top_k in generation_config.json for transformers compatibility

04daea5 verified 3 days ago

Raw History Blame Contribute Delete

230 Bytes

{
  "bos_token_id": 0,
  "do_sample": true,
  "eos_token_id": 1,
  "frequency_penalty": 0,
  "max_tokens": 1048576,
  "pad_token_id": 2,
  "presence_penalty": 0,
  "top_k": 50,
  "top_p": 0.95,
  "transformers_version": "4.57.1"
}

	{
	"bos_token_id": 0,
	"do_sample": true,
	"eos_token_id": 1,
	"frequency_penalty": 0,
	"max_tokens": 1048576,
	"pad_token_id": 2,
	"presence_penalty": 0,
	"top_k": 50,
	"top_p": 0.95,
	"transformers_version": "4.57.1"
	}