Text Generation
Transformers
Safetensors
qwen3
embeddings
quantized
w4a16
auto-round
auto-gptq
conversational
text-generation-inference
4-bit precision
Instructions to use groxaxo/octen-embedding-8b-w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use groxaxo/octen-embedding-8b-w4a16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="groxaxo/octen-embedding-8b-w4a16") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("groxaxo/octen-embedding-8b-w4a16") model = AutoModelForCausalLM.from_pretrained("groxaxo/octen-embedding-8b-w4a16") 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
- vLLM
How to use groxaxo/octen-embedding-8b-w4a16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "groxaxo/octen-embedding-8b-w4a16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "groxaxo/octen-embedding-8b-w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/groxaxo/octen-embedding-8b-w4a16
- SGLang
How to use groxaxo/octen-embedding-8b-w4a16 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 "groxaxo/octen-embedding-8b-w4a16" \ --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": "groxaxo/octen-embedding-8b-w4a16", "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 "groxaxo/octen-embedding-8b-w4a16" \ --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": "groxaxo/octen-embedding-8b-w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use groxaxo/octen-embedding-8b-w4a16 with Docker Model Runner:
docker model run hf.co/groxaxo/octen-embedding-8b-w4a16
| { | |
| "base_model": "Octen/Octen-Embedding-8B", | |
| "quant_model": "quantized/octen-embedding-8b-w4a16/auto-round-auto-gptq", | |
| "num_eval_rows": 5, | |
| "base_embedding_dim": 4096, | |
| "quant_embedding_dim": 4096, | |
| "base_recall_at_1": 0.8, | |
| "base_recall_at_5": 1.0, | |
| "quant_recall_at_1": 1.0, | |
| "quant_recall_at_5": 1.0, | |
| "mean_query_cosine_base_vs_quant": 0.9839715957641602, | |
| "mean_doc_cosine_base_vs_quant": 0.9820238351821899, | |
| "recall_at_1_delta": 0.19999999999999996, | |
| "recall_at_5_delta": 0.0 | |
| } |