Text Generation
Transformers
Safetensors
llama
quantized
fp8
sglang
nanbeige
conversational
text-generation-inference
compressed-tensors
Instructions to use ProCreations/Nanbeige4.3-33B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/Nanbeige4.3-33B-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ProCreations/Nanbeige4.3-33B-FP8") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ProCreations/Nanbeige4.3-33B-FP8") model = AutoModelForCausalLM.from_pretrained("ProCreations/Nanbeige4.3-33B-FP8", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ProCreations/Nanbeige4.3-33B-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ProCreations/Nanbeige4.3-33B-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ProCreations/Nanbeige4.3-33B-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ProCreations/Nanbeige4.3-33B-FP8
- SGLang
How to use ProCreations/Nanbeige4.3-33B-FP8 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 "ProCreations/Nanbeige4.3-33B-FP8" \ --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": "ProCreations/Nanbeige4.3-33B-FP8", "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 "ProCreations/Nanbeige4.3-33B-FP8" \ --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": "ProCreations/Nanbeige4.3-33B-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ProCreations/Nanbeige4.3-33B-FP8 with Docker Model Runner:
docker model run hf.co/ProCreations/Nanbeige4.3-33B-FP8
Download VALIDATION.json from ProCreations/Nanbeige4.3-33B-FP8: direct link, hf CLI and curl.
- Browser
- Download file 134 kB
-
https://huggingface.co/ProCreations/Nanbeige4.3-33B-FP8/resolve/main/VALIDATION.json
- Command line
-
hf download hf://ProCreations/Nanbeige4.3-33B-FP8/VALIDATION.json
-
curl -L -o VALIDATION.json https://huggingface.co/ProCreations/Nanbeige4.3-33B-FP8/resolve/main/VALIDATION.json
134 kB
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| "batch": 1, | |
| "input_tokens": 131072, | |
| "output_tokens": 256, | |
| "decode_tok_s": 31.261125635351885, | |
| "ttft_s": 58.35265536597581, | |
| "e2e_aggregate_tok_s": 3.8487662647770065, | |
| "pool_tokens": 278528, | |
| "kv_actual": "torch.float8_e4m3fn" | |
| } | |
| ], | |
| "receipt": "dad8f0cfdc609b357ffdc8d274f3c5ccb89a17ab6b8ba507242e6da1aca19cb9" | |
| }, | |
| "primitive_fp8_comparison": { | |
| "revision": "7aab1aa", | |
| "kv": "fp8_e4m3", | |
| "single_1k_decode_tok_s": 33.42, | |
| "decode_128k_tok_s": 25.41, | |
| "aggregate_128_tok_s_initial_baseline": 1811, | |
| "aggregate_128_tok_s_later_matched": 1854.5, | |
| "aggregate_scope": "1811 is the initial8192-chunk baseline; later2048-chunk matched bank measured1854.5. These are not interchangeable exact-protocol concurrency controls.", | |
| "primitive_actual_fp8_pool_tokens": 497671 | |
| }, | |
| "parser_contract": { | |
| "recommended_reasoning_parser": "nanbeige", | |
| "recommended_tool_call_parser": "nanbeige", | |
| "n": 52, | |
| "passed": 44, | |
| "pins": { | |
| "versions": { | |
| "sglang": "0.5.21", | |
| "torch": "2.13.0+cu130", | |
| "transformers": "5.12.1", | |
| "flashinfer-python": "0.6.18", | |
| "sglang-kernel": "0.4.7" | |
| }, | |
| "artifact": "fp8-block-calib-v1", | |
| "config_sha256": "e33990cbaf0e1cb7e261dee24c373e62fc8c1daa384e1a4787890adb5c9ea127", | |
| "manifest_sha256": "5f3d0a025474f103d963541841d934f619b917f47d12fd547a3ed0f5d474ae98", | |
| "script_sha256": "11bbda662b642c9efeca87e71941728d54da0ff595d995e776246a89fc0ae59c", | |
| "aliases": { | |
| "reasoning_same_class": true, | |
| "tool_same_class": true | |
| }, | |
| "methods": "Native OpenAI endpoint, sampled output IDs via sglext decoded with skip_special_tokens=False. Natural cases plus separately labeled stock-regex wire fixtures. No parser or kernel patches." | |
| }, | |
| "known_limitations": [ | |
| "Literal </parameter> inside an XML string parameter terminates it early in stock Qwen3CoderDetector, affecting both naming aliases and stream/non-stream." | |
| ] | |
| } | |
| } | |