Text Generation
Transformers
Safetensors
qwen3
feature-extraction
speculative-decoding
dspark
dflash
specforge
sglang
custom_code
text-generation-inference
Instructions to use RadixArk/Inkling-DSpark-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RadixArk/Inkling-DSpark-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RadixArk/Inkling-DSpark-Preview", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RadixArk/Inkling-DSpark-Preview", trust_remote_code=True) model = AutoModel.from_pretrained("RadixArk/Inkling-DSpark-Preview", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RadixArk/Inkling-DSpark-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RadixArk/Inkling-DSpark-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RadixArk/Inkling-DSpark-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RadixArk/Inkling-DSpark-Preview
- SGLang
How to use RadixArk/Inkling-DSpark-Preview 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 "RadixArk/Inkling-DSpark-Preview" \ --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": "RadixArk/Inkling-DSpark-Preview", "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 "RadixArk/Inkling-DSpark-Preview" \ --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": "RadixArk/Inkling-DSpark-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RadixArk/Inkling-DSpark-Preview with Docker Model Runner:
docker model run hf.co/RadixArk/Inkling-DSpark-Preview
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library_name: transformers
pipeline_tag: text-generation
tags:
- speculative-decoding
- dspark
- dflash
- specforge
- sglang
inference: false
---
# Inkling DSpark speculator
## Overview
A DSpark speculator for the Inkling NVFP4 target, enabling faster inference through speculative decoding. DSpark extends the DFlash parallel-draft backbone with a Markov logit-bias head and a per-position confidence head. This checkpoint was trained with [SpecForge](https://github.com/sgl-project/SpecForge/) using hidden states from
a live SGLang target engine.
**Preview:** This is a preview checkpoint. Official training and evaluation are in progress.
## Model Specifications
- **Base model:** Inkling NVFP4; the exact matching target checkpoint and tokenizer are required.
- **Format:** Safetensors (single-file BF16, 1.93B trainable parameters + embedding + lm_head).
- **Draft:** 5 layers (Qwen3-style GQA), hidden 6144, 64 heads / 16 KV heads, head_dim 64, FFN 12288, rope_theta 8000000, `block_size=7`.
- **Vocabulary:** 200,058 tokenizer entries and 201,024 padded weight rows; `mask_token_id=200064`.
- **DSpark heads:** Markov rank 256 (vanilla) and confidence head (with-Markov).
- **Aux hidden-state layers:** `[5, 17, 35, 47, 59]`.
- **Trained context:** sequence length 4096.
- **Target weights:** target embedding and unembedding weights are not included in this checkpoint.
## Evaluation Results
Acceptance length (`acc_len`) at temperature 0:
| Dataset | acc_len |
|:--|--:|
| GSM8K | 4.7585 |
| MATH500 | 4.1830 |
| HumanEval | 4.0504 |
| MBPP | 4.0577 |
| AIME25 | 3.4986 |
| MT-Bench | 3.2215 |
| LiveCodeBench | 3.1384 |
| Alpaca | 3.0479 |
| Arena-Hard-v2 | 3.0280 |
| **Mean** | **3.6649** |
## Serving with SGLang
Requires a SGLang build (`docker pull lmsysorg/sglang:dev-cu13-inkling-dspark`) with DSpark support:
```bash
SGLANG_ENABLE_UNIFIED_RADIX_TREE=1 \
python -m sglang.launch_server \
--trust-remote-code \
--model-path thinkingmachines/Inkling-NVFP4 \
--tp 8 \
--quantization modelopt_fp4 \
--attention-backend fa4 \
--page-size 128 \
--fp4-gemm-backend flashinfer_trtllm \
--moe-runner-backend flashinfer_trtllm_routed \
--enable-torch-symm-mem \
--mamba-radix-cache-strategy extra_buffer \
--mem-fraction-static 0.68 \
--swa-full-tokens-ratio 0.1 \
--mamba-full-memory-ratio 0.1 \
--max-running-requests 68 \
--reasoning-parser inkling \
--tool-call-parser inkling \
--skip-server-warmup \
--speculative-algorithm DSPARK \
--speculative-draft-model-path RadixArk/Inkling-DSpark-Preview \
--speculative-draft-model-quantization unquant \
--chunked-prefill-size 8192 \
--cuda-graph-max-bs-prefill 8192 \
--disable-flashinfer-autotune \
--host 0.0.0.0 \
--port 30000
```
## Training Details
- **Framework:** SpecForge online distillation with hidden states captured from the frozen Inkling target.
- **Data:** 400K Inkling regenerations from `open-perfectblend`.
- **Schedule:** up to 10 epochs, AdamW, peak learning rate `6e-4`, cosine decay, 4% warmup, and gradient clipping at 1.0.
- **Loss:** `0.1 CE + 0.9 L1 distillation + 1.0 confidence BCE`, with 512 sampled anchors per sequence and `block_size=7`.
|