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b1rd/GLM-5.2-speculator.dspark-quanttrio-int4-ft
GLM-5.2-speculator.dspark-quanttrio-int4-ft is a machine learning model from b1rd. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
A finetune of RedHatAI/GLM-5.2-speculator.dspark re-aligned to the hidden states of the int4/int8 target QuantTrio/GLM-5.2-Int4-Int8Mix.
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From the Hugging Face model README
A finetune of RedHatAI/GLM-5.2-speculator.dspark re-aligned to the hidden states of the int4/int8 target QuantTrio/GLM-5.2-Int4-Int8Mix.
The public speculator was trained against FP8-GLM hidden states; served against the QuantTrio quant it accepts ~1.45 extra tokens/step. This finetune reaches ~2.1, which on a 4× DGX Spark (GB10) cluster is the difference between ~19.6 and ~24.7 tok/s decode at up to 1,048,576 context — lossless (exact rejection sampling), acceptance flat in depth.
Full serving recipe: bird/GLM-spark → recipes/4x-dspark-1m
(vLLM branch bird/vllm-lil@dspark-ring-1m-20260711
with ring-buffer windowed drafting — required; stock vLLM's full-context
DSpark port suffers acceptance depth-collapse).
| path | what |
|---|---|
model.safetensors + config.json | full-vocab finetune — exact drop-in for the RedHatAI original (same speculators layout) |
ft32k/ | reduced draft vocab (top-32,768 ids, 99.94% coverage, d2t mapping) — what our production runs; same speed, ~400 MB/rank less memory |
--speculative-config '{"model":"/path/to/this-repo","num_speculative_tokens":3,
"attention_backend":"TRITON_ATTN","quantization":"fp8"}'
# plus: VLLM_DSPARK_DRAFT_RING=1 VLLM_DSPARK_DRAFT_WINDOW=1024
num_speculative_tokens must keep 1+k a power of two on the b12x path
(k∈{3,7}); k=3 is optimal.
Teacher-forced on ~3M tokens of self-generated capture from the target quant
itself (raw per-step target aux hidden states + token ids recorded while
serving natural technical text at depths up to ~880k tokens), using the
pipeline published in the recipe (speculator-training/): stitched
full-window streams, bonus-anchor fill-in readout, window-excludes-anchor,
length-weighted depth sampling, best-rolling-checkpoint selection. 2,000
steps, 3× GB10 DDP, ~75 minutes. Numerically verified against the inference
kernels (bit-exact hidden-norm, bf16-noise rope at 1M positions).
Quality is unaffected by construction: DSpark verification is exact rejection sampling against the target.