Text Generation
Transformers
Safetensors
qwen3
fp8
compressed-tensors
vllm
quantized
custom_code
text-generation-inference
Instructions to use liodon-ai/Nanbeige4.2-3B-DSpark-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liodon-ai/Nanbeige4.2-3B-DSpark-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="liodon-ai/Nanbeige4.2-3B-DSpark-FP8", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("liodon-ai/Nanbeige4.2-3B-DSpark-FP8", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("liodon-ai/Nanbeige4.2-3B-DSpark-FP8", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use liodon-ai/Nanbeige4.2-3B-DSpark-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liodon-ai/Nanbeige4.2-3B-DSpark-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liodon-ai/Nanbeige4.2-3B-DSpark-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/liodon-ai/Nanbeige4.2-3B-DSpark-FP8
- SGLang
How to use liodon-ai/Nanbeige4.2-3B-DSpark-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 "liodon-ai/Nanbeige4.2-3B-DSpark-FP8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liodon-ai/Nanbeige4.2-3B-DSpark-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "liodon-ai/Nanbeige4.2-3B-DSpark-FP8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liodon-ai/Nanbeige4.2-3B-DSpark-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use liodon-ai/Nanbeige4.2-3B-DSpark-FP8 with Docker Model Runner:
docker model run hf.co/liodon-ai/Nanbeige4.2-3B-DSpark-FP8
Add FP8 (dynamic) quantization for Nanbeige4.2-3B-DSpark
Browse files- README.md +68 -0
- config.json +114 -0
- generation_config.json +8 -0
- model.safetensors +3 -0
- recipe.yaml +8 -0
- tokenizer.json +84 -0
- tokenizer_config.json +12 -0
README.md
ADDED
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| 1 |
+
---
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| 2 |
+
license: other
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| 3 |
+
base_model: Nanbeige/Nanbeige4.2-3B-DSpark
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base_model_relation: quantized
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| 5 |
+
library_name: transformers
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| 6 |
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pipeline_tag: text-generation
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| 7 |
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tags:
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| 8 |
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- fp8
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| 9 |
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- compressed-tensors
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| 10 |
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- vllm
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- quantized
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quantized_by: liodon-ai
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---
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| 14 |
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# Nanbeige4.2-3B-DSpark — FP8 (dynamic)
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FP8 quantization of [Nanbeige/Nanbeige4.2-3B-DSpark](https://huggingface.co/Nanbeige/Nanbeige4.2-3B-DSpark), published by [Liodon AI](https://huggingface.co/liodon-ai).
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| 18 |
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| 19 |
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Quantized with [llm-compressor](https://github.com/vllm-project/llm-compressor) using the
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| 20 |
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`FP8_DYNAMIC` scheme: weights are cast to FP8 (E4M3) per-channel ahead of time, activations are
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| 21 |
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quantized to FP8 dynamically per-token at inference time. No calibration dataset is needed for this
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| 22 |
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scheme, so the quantized weights are numerically just a direct cast of the original — no calibration-set
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| 23 |
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bias to worry about. `lm_head` is left unquantized (standard practice — negligible size, disproportionate
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quality impact if quantized).
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Original size: 1.7 GB → Quantized: 2.8 GB.
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| 27 |
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## Quick Start
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**vLLM**
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| 31 |
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```bash
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| 32 |
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vllm serve liodon-ai/Nanbeige4.2-3B-DSpark-FP8
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| 33 |
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```
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| 34 |
+
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| 35 |
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**Text Generation Inference (TGI)**
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| 36 |
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```bash
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| 37 |
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docker run --gpus all -p 8080:80 ghcr.io/huggingface/text-generation-inference \
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| 38 |
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--model-id liodon-ai/Nanbeige4.2-3B-DSpark-FP8
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| 39 |
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```
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| 40 |
+
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| 41 |
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**SGLang**
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| 42 |
+
```bash
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| 43 |
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python -m sglang.launch_server --model-path liodon-ai/Nanbeige4.2-3B-DSpark-FP8
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| 44 |
+
```
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| 45 |
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FP8 execution requires an NVIDIA GPU with compute capability ≥ 8.9 (Ada/Hopper/Blackwell — RTX 40-series,
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| 47 |
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L4/L40S, H100/H200, B100/B200/GB10). On older GPUs, vLLM/TGI will dequantize to run, which loses the
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| 48 |
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speed/memory benefit.
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| 49 |
+
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| 50 |
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## Source
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| 51 |
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| 52 |
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- **Model**: [Nanbeige/Nanbeige4.2-3B-DSpark](https://huggingface.co/Nanbeige/Nanbeige4.2-3B-DSpark)
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| 53 |
+
- **License**: other
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| 54 |
+
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| 55 |
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## Citation
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| 56 |
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| 57 |
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```bibtex
|
| 58 |
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@misc{liodonai_nanbeige4_2_3b_dspark_fp8,
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| 59 |
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title = {Nanbeige4.2-3B-DSpark — FP8},
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| 60 |
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author = {{Liodon AI}},
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| 61 |
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year = {2026},
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| 62 |
+
howpublished = {\url{https://huggingface.co/liodon-ai/Nanbeige4.2-3B-DSpark-FP8}},
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| 63 |
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note = {FP8 (dynamic) quantization of Nanbeige/Nanbeige4.2-3B-DSpark}
|
| 64 |
+
}
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| 65 |
+
```
|
| 66 |
+
|
| 67 |
+
---
|
| 68 |
+
*Quantized by [Liodon AI](https://huggingface.co/liodon-ai)*
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config.json
ADDED
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| 1 |
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{
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| 2 |
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"architectures": [
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| 3 |
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"Qwen3ForCausalLM"
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| 4 |
+
],
|
| 5 |
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"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoModel": "dspark.DSparkDraftModel"
|
| 9 |
+
},
|
| 10 |
+
"block_size": 7,
|
| 11 |
+
"bos_token_id": 166100,
|
| 12 |
+
"dflash_config": {
|
| 13 |
+
"attention_mode": "gqa",
|
| 14 |
+
"confidence_head_alpha": 1.0,
|
| 15 |
+
"confidence_head_with_markov": true,
|
| 16 |
+
"enable_confidence_head": true,
|
| 17 |
+
"markov_head_type": "vanilla",
|
| 18 |
+
"markov_rank": 256,
|
| 19 |
+
"mask_token_id": 166143,
|
| 20 |
+
"projector_type": "dspark",
|
| 21 |
+
"target_layer_ids": [
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| 22 |
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1,
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| 23 |
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10,
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| 24 |
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21,
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| 25 |
+
32,
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| 26 |
+
40
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| 27 |
+
]
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| 28 |
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},
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| 29 |
+
"dtype": "bfloat16",
|
| 30 |
+
"eos_token_id": 166101,
|
| 31 |
+
"head_dim": 128,
|
| 32 |
+
"hidden_act": "silu",
|
| 33 |
+
"hidden_size": 3072,
|
| 34 |
+
"initializer_range": 0.02,
|
| 35 |
+
"intermediate_size": 10752,
|
| 36 |
+
"layer_types": [
|
| 37 |
+
"full_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
+
"full_attention",
|
| 40 |
+
"full_attention",
|
| 41 |
+
"full_attention"
|
| 42 |
+
],
|
| 43 |
+
"markov_head_type": "vanilla",
|
| 44 |
+
"markov_rank": 256,
|
| 45 |
+
"max_position_embeddings": 262144,
|
| 46 |
+
"max_window_layers": 5,
|
| 47 |
+
"model_type": "qwen3",
|
| 48 |
+
"num_attention_heads": 48,
|
| 49 |
+
"num_hidden_layers": 5,
|
| 50 |
+
"num_key_value_heads": 8,
|
| 51 |
+
"num_target_layers": 44,
|
| 52 |
+
"pad_token_id": 0,
|
| 53 |
+
"quantization_config": {
|
| 54 |
+
"config_groups": {
|
| 55 |
+
"group_0": {
|
| 56 |
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"format": "float-quantized",
|
| 57 |
+
"input_activations": {
|
| 58 |
+
"actorder": null,
|
| 59 |
+
"block_structure": null,
|
| 60 |
+
"dynamic": true,
|
| 61 |
+
"group_size": null,
|
| 62 |
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"num_bits": 8,
|
| 63 |
+
"observer": null,
|
| 64 |
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"observer_kwargs": {},
|
| 65 |
+
"scale_dtype": null,
|
| 66 |
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"strategy": "token",
|
| 67 |
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"symmetric": true,
|
| 68 |
+
"type": "float",
|
| 69 |
+
"zp_dtype": null
|
| 70 |
+
},
|
| 71 |
+
"output_activations": null,
|
| 72 |
+
"targets": [
|
| 73 |
+
"Linear"
|
| 74 |
+
],
|
| 75 |
+
"weights": {
|
| 76 |
+
"actorder": null,
|
| 77 |
+
"block_structure": null,
|
| 78 |
+
"dynamic": false,
|
| 79 |
+
"group_size": null,
|
| 80 |
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"num_bits": 8,
|
| 81 |
+
"observer": "memoryless_minmax",
|
| 82 |
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"observer_kwargs": {},
|
| 83 |
+
"scale_dtype": null,
|
| 84 |
+
"strategy": "channel",
|
| 85 |
+
"symmetric": true,
|
| 86 |
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"type": "float",
|
| 87 |
+
"zp_dtype": null
|
| 88 |
+
}
|
| 89 |
+
}
|
| 90 |
+
},
|
| 91 |
+
"format": "float-quantized",
|
| 92 |
+
"global_compression_ratio": null,
|
| 93 |
+
"ignore": [
|
| 94 |
+
"lm_head"
|
| 95 |
+
],
|
| 96 |
+
"kv_cache_scheme": null,
|
| 97 |
+
"quant_method": "compressed-tensors",
|
| 98 |
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"quantization_status": "compressed",
|
| 99 |
+
"sparsity_config": {},
|
| 100 |
+
"transform_config": {},
|
| 101 |
+
"version": "0.18.0"
|
| 102 |
+
},
|
| 103 |
+
"rms_norm_eps": 1e-05,
|
| 104 |
+
"rope_parameters": {
|
| 105 |
+
"rope_theta": 70000000,
|
| 106 |
+
"rope_type": "default"
|
| 107 |
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},
|
| 108 |
+
"sliding_window": null,
|
| 109 |
+
"tie_word_embeddings": false,
|
| 110 |
+
"transformers_version": "5.14.1",
|
| 111 |
+
"use_cache": true,
|
| 112 |
+
"use_sliding_window": false,
|
| 113 |
+
"vocab_size": 166144
|
| 114 |
+
}
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generation_config.json
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{
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| 2 |
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"_from_model_config": true,
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| 3 |
+
"bos_token_id": 166100,
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| 4 |
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"eos_token_id": 166101,
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| 5 |
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"pad_token_id": 0,
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| 6 |
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"transformers_version": "5.14.1",
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| 7 |
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"use_cache": true
|
| 8 |
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:78f5d5c115114a5f1ede1f97f19ba001756cc4e709a9d731c60caa4f0c9a5a60
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| 3 |
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size 2757670040
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recipe.yaml
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default_stage:
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default_modifiers:
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| 3 |
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QuantizationModifier:
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| 4 |
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targets: [Linear]
|
| 5 |
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ignore: [lm_head]
|
| 6 |
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scheme: FP8_DYNAMIC
|
| 7 |
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bypass_divisibility_checks: false
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| 8 |
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requires_calibration_data: false
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tokenizer.json
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"version": "1.0",
|
| 3 |
+
"truncation": null,
|
| 4 |
+
"padding": null,
|
| 5 |
+
"added_tokens": [
|
| 6 |
+
{
|
| 7 |
+
"id": 0,
|
| 8 |
+
"content": "<|endoftext|>",
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"lstrip": false,
|
| 11 |
+
"rstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"special": true
|
| 14 |
+
}
|
| 15 |
+
],
|
| 16 |
+
"normalizer": {
|
| 17 |
+
"type": "NFC"
|
| 18 |
+
},
|
| 19 |
+
"pre_tokenizer": {
|
| 20 |
+
"type": "Sequence",
|
| 21 |
+
"pretokenizers": [
|
| 22 |
+
{
|
| 23 |
+
"type": "Split",
|
| 24 |
+
"pattern": {
|
| 25 |
+
"Regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
|
| 26 |
+
},
|
| 27 |
+
"behavior": "Isolated",
|
| 28 |
+
"invert": false
|
| 29 |
+
},
|
| 30 |
+
{
|
| 31 |
+
"type": "ByteLevel",
|
| 32 |
+
"add_prefix_space": false,
|
| 33 |
+
"trim_offsets": true,
|
| 34 |
+
"use_regex": false
|
| 35 |
+
}
|
| 36 |
+
]
|
| 37 |
+
},
|
| 38 |
+
"post_processor": {
|
| 39 |
+
"type": "TemplateProcessing",
|
| 40 |
+
"single": [
|
| 41 |
+
{
|
| 42 |
+
"Sequence": {
|
| 43 |
+
"id": "A",
|
| 44 |
+
"type_id": 0
|
| 45 |
+
}
|
| 46 |
+
}
|
| 47 |
+
],
|
| 48 |
+
"pair": [
|
| 49 |
+
{
|
| 50 |
+
"Sequence": {
|
| 51 |
+
"id": "A",
|
| 52 |
+
"type_id": 0
|
| 53 |
+
}
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"Sequence": {
|
| 57 |
+
"id": "B",
|
| 58 |
+
"type_id": 1
|
| 59 |
+
}
|
| 60 |
+
}
|
| 61 |
+
],
|
| 62 |
+
"special_tokens": {}
|
| 63 |
+
},
|
| 64 |
+
"decoder": {
|
| 65 |
+
"type": "ByteLevel",
|
| 66 |
+
"add_prefix_space": true,
|
| 67 |
+
"trim_offsets": true,
|
| 68 |
+
"use_regex": true
|
| 69 |
+
},
|
| 70 |
+
"model": {
|
| 71 |
+
"type": "BPE",
|
| 72 |
+
"dropout": null,
|
| 73 |
+
"unk_token": null,
|
| 74 |
+
"continuing_subword_prefix": "",
|
| 75 |
+
"end_of_word_suffix": "",
|
| 76 |
+
"fuse_unk": false,
|
| 77 |
+
"byte_fallback": false,
|
| 78 |
+
"ignore_merges": false,
|
| 79 |
+
"vocab": {
|
| 80 |
+
"<|endoftext|>": 0
|
| 81 |
+
},
|
| 82 |
+
"merges": []
|
| 83 |
+
}
|
| 84 |
+
}
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": null,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": null,
|
| 5 |
+
"eos_token": "<|endoftext|>",
|
| 6 |
+
"is_local": false,
|
| 7 |
+
"local_files_only": false,
|
| 8 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 9 |
+
"pad_token": "<|endoftext|>",
|
| 10 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 11 |
+
"unk_token": "<|endoftext|>"
|
| 12 |
+
}
|