Calls the actual ggml_vec_dot_dt3_q8_0_generic symbol against the
dispatched vec_dot and requires memcmp-equal floats. Blocks exercise
the three regions, the 79/80 and 119/120 boundaries, non-trivial qh
bytes (would expose a vectorization reading their padding 5th digit),
and scales of both and mixed signs; y reaches the full q8_0 range.
Mutation-checked: flipping one bit of a digit blend mask in the
AVX-512 kernel makes 94 of the 96 reps fail.
Decode both planes of a block with VBMI byte permutes: the *3 multiply
chain (wrapping, so it commutes with the permutation) is computed once
on the whole 56-byte block, and masked vpermb picks each element's byte
from the chain vector of its base-3 digit. The qh lanes never see 3^4,
which would read the padding 5th digit of the qh bytes. The trits reach
the integer product as xi in {0, 1, 2} via the same avg trick as
tq1_0, with VNNI dpbusd against the q8_0 bytes and sum(y) subtracted.
The per-q8_0-block sums and the float accumulation keep the exact
operation order of the generic implementation, so the result is
bit-identical to it (checked by test-dt3).
2.2x over the (autovectorized) generic on a Ryzen AI 9 HX 370.
The dequantize + cuBLAS fallback computes in fp16 (CUBLAS_COMPUTE_16F)
on fast-fp16 hardware and in TF32 under
GGML_CUDA_CUBLAS_COMPUTE_TYPE=f32, so no analytic tolerance separates
'correct' from 'broken' there without also tracking cuBLAS numerics.
What IS ours to guarantee: the fallback must behave exactly as if the
weights were an F16 tensor holding fp16(dequant(block)). Gate on that
bit-identity and demote the analytic GEMM errors to INFO.
Elementwise max relative error explodes on cancellation whenever a true
output element is near zero, so the mul_mat checks now gate on the
relative Frobenius norm and keep the max as information. The GEMM
fallback dequantizes to fp16 on fast-fp16 hardware and DT3 weights
(d1*t1 + d2*t2) are generally not fp16-representable, so that path is
judged against a reference computed from fp16-rounded weights (taking
the better of both references so GGML_CUDA_CUBLAS_COMPUTE_TYPE=f32 also
passes). Q4_1 (same non-fp16-exact regime) and Q4_0 (fp16-exact weights,
pure GEMM error floor) run through the identical comparison as controls.
Checks the GPU backend against the validated CPU path: GET_ROWS
dequantization must match dequantize_row_dt3 bit by bit on directed
blocks (region boundaries 79/80 and 119/120, qh elements, negative
scales) and on raw random bytes; MUL_MAT must match a double precision
reference from the dequantized weights, with activations whose q8_1
quantization is exact, plus a manual trit-sum check with non-trivial qh.
Skips cleanly when no GPU backend is available.
vec_dot_dt3_q8_1 processes a whole 128-element DT3 block per call
(VDR_DT3_Q8_1_MMVQ = 4, QI_DT3 = 4), i.e. the 4 q8_1 chunks it spans,
with one pair of integer accumulators per chunk:
sum_j d8[j] * (d1*sumi1[j] + d2*sumi2[j])
The trit decode reuses ggml_cuda_dt3_get_trit with fully unrolled loops,
so all indices and pow3 factors fold into constants; no __byte_perm or
other NVIDIA-only intrinsics. Enables MUL_MAT in supports_op: ncols_dst
<= 8 takes MMVQ, larger falls back to dequantization + cuBLAS (no MMQ
tile kernel yet).
Decode one packed ternary plane with the shared ggml_cuda_dt3_get_trit
helper (shifts, masks and a small pow3 table; the uint8_t wrap-around of
the intermediate product is intentional and matches the CPU reference).
The qh bytes hold only 4 trits; their 5th base-3 digit is packing padding
that always decodes to -1 and is never read.
Wires DT3 into the generic dequantize_block templates (to fp32/fp16/bf16,
contiguous and not) and into get_rows, and enables GET_ROWS in
supports_op.
The previous byte-position test packed with the test's own packer on
both sides of the comparison, so it exercised none of the library code.
It now pins hand-computed byte values (43/100/127/42/124...) at the
region boundaries (79/80, 119/120) as ground truth and drives both
directions through the library: to_float must place each literal byte's
trit at the exact element, and from_float must produce the exact literal
byte, for both planes.
Also probes ggml_validate_row_data over all 256 byte values in qs and
qh positions (must accept exactly the 243/81 reachable codes), the
all-0xaa block, and a well-formed packed block.
The reference-quantizer disclaimer only existed in the code and in
llama-quantize --help; now it is also printed where the mistake would
actually be made, at the start of a quantization run targeting DT3.
ggml_validate_row_data now rejects unreachable code bytes: the ceiling
division packing reaches only 243 of the 256 byte values in qs and 81
in qh (4 trits plus an always-zero padding digit), so corruption that
previously loaded and generated garbage silently is caught at load
time. Previously only the two fp16 scales were checked.
quantize_dt3 no longer discards quant_weights silently: an ignored
imatrix now prints a loud warning (once), otherwise an imatrix A/B on
DT3 would come out byte-identical and invite the false conclusion that
the imatrix does nothing.
The two initial trit passes of quantize_row_dt3_ref now clamp like the
refit passes do, so a NaN input cannot push an out-of-range value from
lroundf into the packer.
test-dt3 checks the layout against an independent packer written from
the format spec: single-trit position mapping for all 256 (plane, pos)
pairs, structural byte-position checks at the region boundaries
(79/80, 119/120), exact round-trips with negative scales, byte parity
of the in-tree quantizer on already-ternary inputs, and the vec_dot
against a hand-made sum over the known trits (catches any path that
reads the padding 5th trit of the qh bytes).
test-dt3-rust-parity.py packs known trits with ternaria's pack_dt3 and
verifies that dequantize_row_dt3 (via test-dt3 --dequant) reproduces
d1*t1 + d2*t2 bit-exactly.
Also wires DT3 into the test-quantize-fns thresholds (ternary class).
Registers the type id, file type and block size, and implements numpy
dequantization (verified bit-exact against the C implementation with
gguf-py/tests/test_quants.py, including random byte payloads).
Quantization is intentionally left unimplemented, like the K-quants:
DT3 planes come from an external solver (PTQTP) and are packed
directly, so a from-float numpy path would only invite quantizing
models with the wrong algorithm.
Adds LLAMA_FTYPE_MOSTLY_DT3 at the end of the ftype enum, the loader
name/guess mappings, the quantization fallbacks (same as the other
ternary types), and the llama-quantize table entry. The table entry
warns that the in-tree quantizer is only the reference one: DT3 models
with the measured quality are produced by the external PTQTP pipeline.
The vec_dot pairs DT3 with Q8_0 (4 q8_0 blocks per DT3 block) and keeps
one integer accumulator per plane: sumf += dy * (d1*sumi1 + d2*sumi2).
Q8_0 instead of Q8_K on purpose: the planes are symmetric ternary so the
q8_K bsums are dead weight, and 32-element blocks accept any row size
that is a multiple of 128.
Trit decoding reuses unpack_plane_dt3, which reads only 4 trits per qh
byte; the 5th base-3 digit of those bytes is packer padding that always
decodes to -1 and must never be read.
Add the dual-plane ternary DT3 type to the type registry along with its
reference row functions. Each of the two planes is packed exactly like
tq1_0 with all constants halved (block of 128 elements): qs 48 -> 24
bytes over two passes of 16 and 8 bytes, qh 4 -> 2 bytes.
The trit decoding lives in a single exported helper (unpack_plane_dt3)
so that dequantization and the upcoming CPU vec_dot share it.
The reference quantizer is a greedy two-pass (plane 1 by absolute max,
plane 2 on the residual) plus two rounds of alternating least-squares
refits. It is intentionally NOT the PTQTP solver used to produce the
published DT3 models.
DT3 stores w_i = d[0]*t0_i + d[1]*t1_i with t in {-1,0,+1}, two ternary
planes over a 128-element block: 56 bytes, 3.5 bpw exactly.
Each plane uses the tq1_0 base-3 packing with every constant halved for
the smaller block (qs 48->24 B, qh 4->2 B, qs passes over 16 then 8
bytes instead of 32 then 16), which tiles 128 with no leftover bytes.
Reducing tq1_0 to 128 without halving the passes does not tile: with a
24-byte qs the first pass covers nothing and the second overruns.
Upstream merged the Windows argument quoting fix, the NetBSD build
fix and the chdir fallback for glibc older than 2.29, so pin the
vendored copy to a commit that carries all three and remove the
patch files along with the apply step in the sync script.
The new pin also brings the exec error report on glibc older than
2.24 and the ENOSYS mapping to a dedicated error code. Both are
additive and no caller inspects those values.
* Restore quantization of mmprojs
This was lost in the refactor undertaken in #22004.
* add noreturn
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* ci: Add support for CUDA 13.4 ARM64 builds for Windows
Added an architecture-specific CUDA 13.4 Windows build entry targeting ARM64.
Added a CMake configuration to enable ARM64 CUDA cross-compilation from an x64 Windows environment using the x64-hosted CUDA and MSVC toolchain while linking against the ARM64 CUDA import libraries to produce ggml-cuda.dll.
Validated the self-hosted Windows x64 workflow, including toolkit acquisition, CMake configuration, ARM64 CUDA cross-compilation, and packaging. Runtime validation was performed separately on a native ARM64 RTX Spark system using TinyLlama 1.1B Q4_K_M to verify the generated binaries.
The ARM64 CUDA job builds only the ggml-cuda.dll backend (LLAMA_BUILD_SERVER=OFF). The release consists of two packages: the main ARM64 release package, which combines the existing ARM64 CPU outputs with ggml-cuda.dll, and a separate runtime package containing the required CUDA runtime libraries (cudart64_13.dll, cublas64_13.dll, and cublasLt64_13.dll).
The CUDA 13.4 setup uses NVIDIA Developer Preview component archives instead of the GA component downloads used by the existing CUDA setups and will require updates once CUDA 13.4 reaches GA.
* ci: cleans up to align with x64 CUDA setup
- Moves CUDA-specific CMake options into matrix defines.
- Keeps the CUB 3DOT2 option only for CUDA 12.4.
- Removes runtime argument construction and the unnecessary server option.
- Aligns ARM64 CUDA runtime packaging with the existing robocopy approach.
- Generalizes the ARM64 release label from CUDA 13.4 to CUDA 13.
* ci: Set CUDA job name as version-architecture pair
* mark as preview
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* granite-switch: add llama.cpp backend (POC, CPU)
New "granite-switch" architecture: a dense, all-attention Granite-4.1
model with N embedded LoRA adapters selected per-token by control tokens.
- gguf-py schema (arch, KV keys, stacked LoRA tensor names) + writer helpers
- conversion/granite.py: GraniteSwitchModel converter (stacks N adapters +
zero base slot into per-projection A/B tensors; emits switch metadata)
- C++ arch registration (llama-arch.{h,cpp}, llama-model.{h,cpp})
- src/models/granite_switch.cpp: load + per-token switched-LoRA graph via
ggml_mul_mat_id over stacked tensors; sticky per-token index + control-token
substitution in llm_graph_input_switch::set_input
- llm_graph_input_switch in src/models/models.h
Runs end-to-end on CPU: convert 3b checkpoint (842 tensors, stacked dim 13)
and generate on both base and control-token paths. Sticky switch state is
single-sequence (POC); full multi-sequence machinery is a follow-up.
* granite-switch: add Mac (Metal) build + mid-sequence switch demo script
Self-contained script to build llama.cpp on Apple Silicon (Metal),
convert the composed 3b checkpoint, and run the crisp mid-sequence
adapter-switch demos verified on Vela:
- answerability: <|answerability|> mid-seq -> "unanswerable"
- query_rewrite: <|query_rewrite|> mid-seq -> {"rewritten_question": ...}
Each demo runs the same prompt twice, differing only by a control token
placed before the assistant turn, so the per-token switch is visible.
* granite-switch mac demo: add -no-cnv so each run is one-shot
The composed model ships a chat template, so llama-completion auto-enables
interactive conversation mode and halts at a `>` prompt after generating,
stalling the script. -no-cnv disables conversation mode: generate once from
the raw prompt and exit (also prints special tokens, making the switch visible).
* granite-switch: replace global sticky index with in-graph router attention
The POC computed the per-token adapter index on the CPU and carried it
across ubatches in ONE global `mutable int32_t poc_sticky_index`, reset
only when a ubatch contained sequence position 0. That global had two
problems:
1. Concurrency: with multiple sequences in a batch it was last-writer-
wins — one sequence's adapter leaked into the others.
2. Multi-turn: an interactive `ollama run` chat continues one KV cache,
so turn 2 never saw position 0 and the index never reset — the
adapter stayed stuck on across turns.
Port the vLLM/HF backend mechanism faithfully: a single-head causal
"router" attention recovers the adapter index in-graph. Per token, only
dim 0 carries signal — Q[0]=1, K[0]=+gain for a control token / -gain
otherwise, V[0]=adapter slot / 0 — and the causal softmax over the single
visible control token recovers that adapter's slot (readback =
clamp(round(V[0]), 0, n_adapters)). gain=15 matches config.py and is
F16-safe (no F32 cache).
The router's K/V live in the model KV cache at an extra layer
R == hparams.router_layer (== n_layer). We bump n_layer_all to n_real+1
so the cache allocator gives the router its own per-sequence slot, and
set n_layer_nextn=1 so n_layer() stays n_real — the decoder loop and
tensor loading are untouched and never reference layer R. The router K is
exempted from the k-shift RoPE loop (its dim-0 value is a literal
magnitude, not a rotation).
Because the selection now lives in the per-sequence KV cache, CONCURRENT
requests are isolated for free (problem 1 fixed; verified by
scratch/concurrent_switch_test.cpp). set_input becomes stateless pure
per-token maps; the global is gone.
Single-switch contract / known limitation, identical to vLLM & HF: the
gain is flat (no recency), so within one sequence there is no mechanism to
revert to base mid-sequence — once an adapter fires it stays on until that
sequence ends (problem 2 is therefore NOT fixed by a faithful copy; vLLM/HF
avoid it only because each served request is a fresh sequence). A client
continuing one KV cache across turns must start a fresh sequence per turn,
or opt into a recency-biased router (a deliberate divergence, not done
here). Documented in granite_switch.cpp and asserted by
scratch/multiturn_leak_test.cpp.
Verified (CPU): both demos unchanged (answerability -> "unanswerable",
query_rewrite -> rewritten query); concurrent two-sequence isolation
passes; multi-turn carry-over matches the vLLM/HF contract.
* granite-switch: drop scratch tests and mac demo for upstream PR
Remove the local-only development artifacts that should not ship in the
upstream PR:
- granite-switch-mac-demo.sh (local Metal build + demo driver)
- scratch/concurrent_switch_test.cpp
- scratch/multiturn_leak_test.cpp
Also drop the now-dangling reference to the scratch tests from the
granite_switch.cpp header comment. Leaves only the core architecture
support (conversion, gguf constants, llama-arch/model/kv-cache, and the
granite_switch graph).
* granite-switch: trim comments to match native llama.cpp style
* granite-switch: trim conversion comments to match native style
* granite-switch: drop unused adapter_ranks metadata
* granite-switch: rename arch to graniteswitch and drop obid alias
* granite-switch: fix non-ASCII comments and document router gain assumption
* granite-switch: drop section comments from constants.py to match native style
* granite-switch: add functional tensor block comments matching Granite4 Vision style
* granite-switch: clarify n_expert_used comment
State the actual constraint: mul_mat_id needs n_expert_used == 1, and
since the GGUF carries expert_count = 0 the generic loader's
n_expert == 0 => n_expert_used == 0 assertion has already passed by the
time load_arch_hparams runs, so it is forced to 1 here.
* granite-switch: note n_layer_nextn reuse has no MTP
The router carving reuses n_layer_nextn, normally the MTP/next-token
count. Clarify in the comment that it is borrowed here purely as the
trailing-layers lever and that there is no MTP head, to spare readers
the double-take.
* granite-switch: rename source file and apply review nits
* granite-switch: don't force LoRA tensors to F16, follow --outtype instead
* granite-switch: drop redundant _permute_qk wrapper, call LlamaModel.permute directly
* granite-switch: read router gain from GGUF (control_token_gain) instead of hardcoding 15.0
* granite-switch: derive n_slots()
* granite-switch: move llm_graph_input_switch into granite-switch.cpp
* granite-switch: cut AI-style narration comments
* granite-switch: collapse multi-line comments
* granite-switch: rename control_token_* maps to adapter_token_*
* granite-switch: cut noise comments
* granite-switch: rename embedded LoRA tensors to <base>.lora_a/lora_b
* granite-switch: GGML_ASSERT token input to avoid UB on embeddings
* granite-switch: TODO for raw embedding input support
* granite-switch: collapse LoRA tensor constants to .lora_a/.lora_b suffix
* granite-switch: drop n_expert_used hack, guard mul_mat_id buft probe
* granite-switch: stop forcing dense expert counts, read from config
* granite-switch: renamed control_token_gain metadata key to router_gain
* granite-switch: trim header comments to match native style
* granite-switch: collapse LoRA tensors to base name + suffix
* granite-switch: inline suffix checks in tensor op resolution
* granite-switch: drop switch-lora struct comment
* granite-switch: guard router layer index and inline n_slots
* granite-switch: group adapter metadata under {arch}.adapters.* namespace
* granite-switch: add hparams.has_rope(il) for KV-shift rope skipping
* granite-switch: skip arch in test-llama-archs (adapter fixture missing, TODO)
* granite-switch: Keys.Adapters namespace + simplify n_slots
* granite-switch: validate substitute token ids against n_vocab
* granite-switch: bound adapter count and lora rank from GGUF
* granite-switch: reject MTP context type when router_layer is set
* granite-switch: throw on bad adapter metadata instead of GGML_ASSERT
* granite-switch: use ASCII +/- in router K signal comment
* granite-switch: document n_layer_nextn repurpose and its leak points
* granite-switch: gate lora_a/lora_b op mapping on router_layer
* granite-switch: label all three preview model sizes
docker info only proves the daemon answers, so the Windows CI passes
the check and then dies trying to run a linux image. The hosted
Windows runners cannot run one: GitHub states the VMs are not enabled
for nested virtualization and will not be, since they already sit one
level deep and the hypervisor does not support more levels
(https://github.com/orgs/community/discussions/25491). Probing the
image itself skips those tests there, and pulls it before the server
waits for the container id.
The saver called add_kv with LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH twice, the
second time passing n_ff_chexp. gguf_set_val_u32 removes-then-appends, so the second
call clobbers the first: the saved shared_feed_forward_length ends up as n_ff_chexp
(0 for every arch except GroveMoE), and expert_chunk_feed_forward_length is never
written at all.
So a save->load roundtrip of any MoE model with a shared expert loses n_ff_shexp. On
reload the arch falls back to n_ff for the shexp tensor shape, that no longer matches
the saved tensor, and the model FAILS to load. Hits qwen2moe, qwen3-next, granite-moe,
hunyuan-moe, ernie4.5, bailingmoe2, nemotron-h, and the other shared-expert MoEs.
Fix: the second call writes LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH.
test-llama-archs: set expert_shared_feed_forward_length to a value distinct from n_ff
in the MoE setup so the roundtrip exercises it. Without the fix the reload fails on a
shexp tensor-shape mismatch; with it, every arch roundtrips clean.
* Update build-sanitize.yml
* make it run on pr
* fix thread
* Update build-sanitize.yml
* Update build-sanitize.yml
* just run thread on github machine
The picker mounts whenever a cwd-aware builtin tool is enabled, so
it can open while file_glob_search is not served or was disabled by
the user. Every typed query then fired a search that could only
fail with a raw error.
Gate the debounced search on the tool state, the same way the
mention picker does, and show a message in place of the results
list that explains why search is unavailable. Manual entry with
Enter still commits a directory. The Browse button and the search
scope footer are hidden as well: Browse resolves the picked folder
name through file_glob_search, and the client-side toggle would not
stop that call.
* server: report the isolate working directory from get_info
Without an explicit cwd, get_info fell back to the server process
working directory even when a tools runtime was configured. That named a
host path no tool would ever run in, since an isolate starts in a
directory of its own.
It now asks the isolate for its working directory in that case, and
keeps the process one only when the tools run on the host.
* remove redundant comment
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
The working directory chip showed up as soon as the server exposed any
builtin tool, so a server started with just get_datetime, or a user who
turned every filesystem tool off in the settings, still got a control
that nothing would read.
Tools now declare whether they resolve their paths and run against the
working directory, next to the write permission they already publish in
the /tools listing. The WebUI shows the chip and enables the /cwd
command only when at least one such tool is both served and left
enabled.
get_output runs the waveform work the pipeline defers to it, from a
single trailing window to a full pass depending on the model. Measuring
it keeps the reported total and the audio to process ratio honest.
* feat: Add contenteditable tokenizer for badge/code-chip chat input
* feat: Add source-space undo/redo history for the rich input
* feat: Split text glued to a closing code fence onto its own line
* feat: Add ChatFormContenteditable rich input renderer
* feat : wire the contenteditable into ChatForm with auto-switch gating
* base : slash-command/misc foundation - model icon and focus-selector constants
* feat : slash-command picker and command parsing helpers
* refactor : wire command and @-mention pickers into the chat form
* ui : improve model selector keyboard navigation and load/dismiss
* feat: Unify markdown/raw-text rendering under one setting with migration
* fix: Misc fixes - tool-call subtitle, assistant wrap, progress guards
* feat: Clamp and style numeric settings inputs from registry bounds
ggml_metal_op_norm sized the threadgroup with
`nth = std::min(nth, args.ne00_t)`, which can leave nth not a multiple of
the simdgroup size. The kernels finish their row reduction with a
cross-simdgroup step where each lane of the last simdgroup reads one
per-simdgroup partial sum out of shmem_f32:
if (tiisg == 0) { shmem_f32[sgitg] = sumf; }
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
When the last simdgroup is partial it has fewer lanes than the
threadgroup has simdgroups, so the tail of the partial sums is never
read and the row sum is too small. For ne00_t = 33 nth becomes 33: two
simdgroups, but only one lane in the second, so one of the two partial
sums is dropped. The mean and variance are then wrong for the whole row.
Round ne00_t up to a whole number of simdgroups instead. Rounding up
rather than dropping the clamp keeps the threadgroup as small as
possible: deleting the line would raise nth to the next power of two
(ne00_t = 544 -> 1024 instead of 544), which costs idle lanes on 26 row
lengths below 8192 that were already correct, including 1536 and 3584.
GGML_OP_NORM is affected as well as GGML_OP_RMS_NORM - both dispatch
through ggml_metal_op_norm.
No mainstream LLM hidden size hits this: ne00_t is ne00/4 on the
vectorized path, so 4096, 8192, 2048 and friends all give a multiple of
32. It is reachable from other norm shapes, e.g. 320-channel norms.
Add NORM and RMS_NORM cases for ne0 = 33, 132 and 260 across the
existing eps values. 33 exercises the scalar path and 132/260 the
vectorized one, since only those divide by 4.
Before, on M3 Pro:
test-backend-ops test -b MTL0 -o NORM 25/50
test-backend-ops test -b MTL0 -o RMS_NORM 26/51
After:
test-backend-ops test -b MTL0 -o NORM 50/50
test-backend-ops test -b MTL0 -o RMS_NORM 51/51
test-backend-ops test -b MTL0 13943/13943
* base : @-mention picker foundation - glob search, picker nav, highlight
* feat : @-mention file/folder picker and mention badges in message bubbles
* fix: Imports
* feat : wire the @-mention picker into the chat form
* fix: Bound the glob-search result cache key and prune stale entries
* webui: load the model selected via ?model=
Opening the WebUI with ?model= selects the model but doesn't load it. The load only starts when you send your first message, so you wait for it then.
This loads it as soon as the page opens, while you're still typing your prompt. It's what the model dropdown already does, and it isn't awaited, so the UI still works while the model loads.
This is the path the Llama macOS app uses to open the WebUI, so it's a common way in.
* webui: gate the load behind ?load=true
Loading on landing is opt-in, so a plain ?model= link behaves as before and doesn't allocate memory on its own.
* webui: name the chat URL params
Collects the query params the chat routes read into a URL_PARAMS constant, instead of repeating the literals across three files. NEW_CHAT_PARAM folds into it.
Millaguie