Text Generation
Transformers
Safetensors
qwen3
feature-extraction
speculative-decoding
dspark
dflash
specforge
sglang
custom_code
text-generation-inference
Instructions to use RadixArk/Inkling-Small-DSpark-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RadixArk/Inkling-Small-DSpark-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RadixArk/Inkling-Small-DSpark-Preview", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RadixArk/Inkling-Small-DSpark-Preview", trust_remote_code=True) model = AutoModel.from_pretrained("RadixArk/Inkling-Small-DSpark-Preview", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RadixArk/Inkling-Small-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-Small-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-Small-DSpark-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RadixArk/Inkling-Small-DSpark-Preview
- SGLang
How to use RadixArk/Inkling-Small-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-Small-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-Small-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-Small-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-Small-DSpark-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RadixArk/Inkling-Small-DSpark-Preview with Docker Model Runner:
docker model run hf.co/RadixArk/Inkling-Small-DSpark-Preview
File size: 3,885 Bytes
0e734cf 024c1ce 0e734cf fcc210d 0e734cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | ---
library_name: transformers
pipeline_tag: text-generation
tags:
- speculative-decoding
- dspark
- dflash
- specforge
- sglang
inference: false
---
# Inkling-Small DSpark speculator
## Overview
A DSpark speculator for the Inkling-Small 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 research preview. Official training and evaluation are in progress.
## Model Specifications
- **Base model:** `thinkingmachines/Inkling-Small-NVFP4`; the exact matching target checkpoint and tokenizer are required.
- **Format:** Safetensors (single-file BF16, 0.90B trainable parameters; draft weights only).
- **Draft:** 5 layers (Qwen3-style GQA), hidden 4096, 64 heads / 16 KV heads, head_dim 64, FFN 8192, rope_theta 8000000, `block_size=15`.
- **RoPE:** YaRN, factor 128, `original_max_position_embeddings=8192`, `max_position_embeddings=1048576` (matches the target addressable range); config carries both `rope_scaling` and `rope_parameters` schemas.
- **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, 11, 23, 29, 35]`.
- **Trained context:** 8,192 native; long-context adaptation with sequences up to 65,536.
- **Target weights:** target embedding and unembedding weights are not included in this checkpoint.
## Evaluation Results
Acceptance length (`acc_len`) at temperature 0 (128 prompts per task, DSPARK block size 7, thinking effort 0.99):
| Dataset | acc_len |
|:--|--:|
| GSM8K | 4.7871 |
| MATH500 | 4.1426 |
| MBPP | 3.4394 |
| HumanEval | 3.3491 |
| MT-Bench | 3.1140 |
| LiveCodeBench | 2.9288 |
| AIME25 | 2.8938 |
| Alpaca | 2.7823 |
| Arena-Hard-v2 | 2.6982 |
| **Mean** | **3.3484** |
## Serving with SGLang
Requires a SGLang build with DSpark support
```bash
SGLANG_ENABLE_UNIFIED_RADIX_TREE=1 \
sglang serve --trust-remote-code \
--model-path thinkingmachines/Inkling-Small-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 skx618/InkLing-Small-DSpark-Preview \
--speculative-draft-model-quantization unquant \
--speculative-dspark-block-size 7 \
--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 a frozen Inkling-Small target served by a colocated per-node TP4 SGLang engine.
- **Stage 1 (original RoPE, 8K):** 444,264 Inkling-Small self-regenerations from `open-perfectblend` (temperature 1.0, top-p 0.95, reasoning effort 0.99), 2 epochs, AdamW, global batch 512, gradient clipping 1.0; peak LR `6e-4` (cosine, 4% warmup).
- **Stage 2 (YaRN-128 adaptation):** weights-only warm start from stage 1; 250,000 chat rows (192,055 previously unseen regenerations + 57,945 replayed) plus 1,092 real 16K-64K agentic trajectories upsampled x8; `max_length=65536`; constant LR `5e-4` with a 64-step re-warm.
- **Loss:** `0.1 CE + 0.9 L1 distillation + 1.0 confidence BCE`, 512 sampled anchors per sequence, `block_size=15`, within-block decay gamma `28/3`.
|