license: mit library_name: radiance pipeline_tag: text-generation tags:
- radiance
- rocm
- amd
- rdna4
- deepseek
- deepseek-v4.1
- uncensored
- abliterated
- tp4
- iq1_s
- mxfp4 base_model:
- deepseek-ai/DeepSeek-V4.1-Flash-1017
DeepSeek-V4.1-Flash-1017, uncensored — radiance container
DeepSeek-V4.1-Flash-1017 as a
single .rad container for the radiance inference
engine (AMD RDNA4, ROCm), with the refusal direction edited out of 18 attention-output projections.
| File | deepseek-v4.1-flash-1017-native-ud-iq1s-abl075.rad — 251.07 GiB |
| Base | DeepSeek-V4.1-Flash-1017 (763B parameters; fp8 weights, fp4 routed experts; MIT) |
| Abliteration | attn_output_b of layers 11–28 re-quantised from the official fp8 at alpha 0.75 of huihui's abliteration strength; the other 18 of 36 layers stay stock. It removes the refusal direction without touching attention q/k/v, the MLP or the Engram tables |
| Quantisation | per-tensor, from Unsloth's UD-IQ1_S mix: routed experts iq1_s/iq2_xxs (gate_up 34 layers iq1_s + 6 iq2_xxs; down 20 + 20), attention and shared experts q6_K/q5_K/q8_0, the Engram table iq4_nl, norms f32, the DSpark drafter's experts MXFP4 as released |
| Drafter | the release's own DSpark mtp.* head (MXFP4 experts, 6.72 GiB) |
| Engram | the 2 Engram layers (103 GiB table) rebuilt from the release tokenizer by data/engram_tables.py; the release ships none |
| Needs | radiance with the DeepSeek-V4.1 architecture and TP4 kernels; four 32 GB cards (R9700 gfx1201) and about 4 GiB of pinned host pool per rank; the Engram table on a disk tier |
| Measured | on the 4×R9700 lab at --max-model-len 409600, greedy essay 50.1 / code 53.4 tok/s (n-gram 5), 16k-token time to first token 4.7 s; 0 GPU resets |
Serve
radiance --model deepseek-v4.1-flash-1017-native-ud-iq1s-abl075.rad \
--tp 4 --tp-wire exact --max-model-len 409600 --max-num-seqs 4 \
--max-num-batched-tokens 4096 --kv-cache-dtype bf16 \
--placement expert_tiered --host-pool-mib 4096 --ngram-placement disk \
--num-speculative-tokens 5 --spec-ngram 5 \
--expert-profile expert-stats.json --expert-pin-top 0.20 \
--host 0.0.0.0 --port 8010
expert-stats.json is radiance's --expert-stats output for this model (per-layer routed-expert
usage); --expert-pin-top 0.20 keeps the hottest fifth resident while the rest streams. Without
it, the same recipe runs with the expert plane on the host pool alone. The Engram table is read
from the disk tier by --ngram-placement disk; it is 103 GiB, so keep it on NVMe.
The server speaks OpenAI's chat API (/v1/chat/completions, /v1/models); the served model name
is DeepSeek-V4.1-Flash. The chat template is embedded in the container.
How this file was made
data/engram_tables.py DeepSeek-V4.1-Flash-1017 engram-tables.safetensors
rad-convert DeepSeek-V4.1-Flash-1017 \
--add engram-tables.safetensors \
--recipe data/recipes/deepseek41-ud-iq1s.recipe \
--imatrix imatrix_unsloth.gguf_file \
-o deepseek-v4.1-flash-1017-native-ud-iq1s.rad
The abliteration is applied after conversion: for layers 11–28, attn_output_b is rebuilt as
W + alpha * (huihui's W - W), then re-quantised to the container's q6_K; alpha 0.75 gives the
abl075 file. The recipe (deepseek41-ud-iq1s.recipe in radiance) names every weight's
quantiser; first match wins, and anything it does not name keeps the checkpoint's own dtype.
iq1_s and iq2_xxs need the imatrix; Unsloth's UD-IQ1_S imatrix is used, with
ffn_gate_exps/ffn_up_exps fused by data/imatrix_merge.py because radiance fuses gate_up.
License
DeepSeek-V4.1-Flash-1017 is MIT licensed (Copyright © DeepSeek). This container is a modified copy: the routed experts and linears are re-encoded as the quantisers above, the Engram table is generated from the tokenizer, and 18 attention-output projections carry the abliteration edit. MIT applies to the original and to these modifications.