loom-fun/bartowski-Qwen2.5-7B-Instruct-loom

Qwen2.5-7B-Instruct (Q4_K_M) for AMD Loom

A Loom package (.loom programs and a model.json card) for the unmodified GGUF checkpoint bartowski/Qwen2.5-7B-Instruct-GGUF (Qwen2.5-7B-Instruct-Q4_K_M.gguf), generated on an AMD Ryzen AI Max+ 395 "Strix Halo" with the loom-skills workflow.

Quick start

pip install "hrx-loom[hf]"
loom-fetch https://huggingface.co/loom-fun/bartowski-Qwen2.5-7B-Instruct-loom/resolve/main/model.json --materialize=copy --output=Qwen2.5-7B-Instruct
loom-serve --model=Qwen2.5-7B-Instruct/model.json --device=amdgpu --memory_bytes=14000000000

The server listens on 127.0.0.1:8080 and answers OpenAI-style /v1/chat/completions requests.

Performance vs llama.cpp

metric	llama.cpp	Loom	margin	roofline	Loom % of roofline	DevLab llama.cpp	DevLab Loom	local ÷ DevLab (llama.cpp, Loom)
decode tok/s, short prompt	45.0	48.6	+8 %	54.8	89 %	41.4	48.1 ¹	1.09, 1.01
decode tok/s, 2k context	36.1	47.8	+32 %	59.3	81 %	36.8	50.9 ¹	0.98, 0.94
decode tok/s, 32k context	27.9	38.0	+36 %	69.6	55 %	27.4	38.1 ¹	1.02, 1.00
prefill tok/s, 1k document	1207.8	1881.6	+56 %	4452.4	42 %	1218.2	1797.8 ¹	0.99, 1.05
prefill tok/s, 2k document	1100.5	1958.8	+78 %	4386.0	45 %	1123.5	1873.5 ¹	0.98, 1.05
prefill tok/s, 8k document	984.3	1754.1	+78 %	4025.6	44 %	999.4	1693.3 ¹	0.98, 1.04
prefill tok/s, 16k document	741.0	1575.3	+113 %	3628.1	43 %	746.3	1469.9 ¹	0.99, 1.07
prefill tok/s, 32k document	531.2	1255.0	+136 %	3029.8	41 %	531.6	1150.0 ¹	1.00, 1.09
4 streams, aggregate decode tok/s	137.5	126.6	-8 %	213.1	59 %	137.2	122.6 ¹	1.00, 1.03
speculative decoding, drafted tokens accepted	—	10 % ²	—	n/a	n/a	n/a	n/a	n/a

• llama.cpp: build llamacpp-rocm/b1369, server arguments -ngl 99 -cram 0 --spec-type ngram-simple -c 33792 -kvu -np 4.
• Loom: speculative decoding on (n-gram prompt lookup, depth 2).
• Same GGUF on both engines, tokens per second. Loom: client-side timing; llama.cpp: server-side timings. Runs per value: decode and prefill 2 (mean), 4 streams 3 (median).
• Measured 2026-11-07; device AMD RYZEN AI MAX+ 395 w/ Radeon 8060S, AMD Radeon 8060S Graphics, Ubuntu 26.04; hrx-loom 0.1.0 (hrx-system d01d1c1); source: local run.
• ¹ Measured 2026-11-07; device AMD RYZEN AI MAX+ 395 w/ Radeon 8060S, AMD Radeon 8060S Graphics, Ubuntu 24.04; hrx-loom 0.1.0 (hrx-system d01d1c1); source: DevLab Dispatch run 42342192972.
• ² Measured 2026-11-08; device AMD RYZEN AI MAX+ 395 w/ Radeon 8060S, AMD Radeon 8060S Graphics, Ubuntu 24.04; hrx-loom 0.1.0 (hrx-system d01d1c1); source: DevLab Dispatch run 42419939771.
• DevLab: the same measurement on an AMD DevLab Dispatch Strix Halo runner; local ÷ DevLab is the local value over the DevLab value of each engine, recorded for comparison, not a verdict.
• Drafted tokens accepted: the share of the drafter's proposed tokens the model accepted during the spec gate's requests.
• Roofline: the hardware ceiling for this GGUF on AMD Ryzen AI Max+ 395 "Strix Halo" (gfx1151) at 216 GB/s and 59 TFLOP/s, from loom-roofline roofline.py (loom-skills a05730e); decode and 4 streams with speculative decoding: verify passes × (1 + acceptance × depth 2) tokens per pass, the drafter's cost not counted; acceptance measured in the row's own session: decode 5 %, decode at 2k 12 %, decode at 32k 50 %, 4 streams 6 %.
• — not measured; n/a not applicable.

Features

feature	status	detail
API	✅	OpenAI-style /v1/chat/completions
tool calling	✅	OpenAI-style tools and tool_calls
reasoning / thinking	❌	not supported
reasoning effort	❌	not supported
speculative decoding	✅	n-gram prompt lookup, depth 2, on by default
image input	❌	not supported
audio input / output	❌	not supported
sampling	✅	temperature, top_p, top_k, seed
context size	✅	32,768 tokens per request
concurrency	✅	4 concurrent requests
max output tokens	✅	4,096 (preset max_tokens)

Correctness

gate	description	result
kernel_tests	Unit tests of the generated kernels against reference implementations	✅ pass (37/37) ¹
golden	llama.cpp reference recorded on the same GGUF: greedy tokens and top-5 log-probs, short prompts and 1k-32k documents	✅ pass ²
compare	Greedy output vs the llama.cpp reference, token by token; every divergence is a near-tie	✅ pass ²
vision	Image input vs llama.cpp with the same projector: identical prompt tokens, replies equal up to near-ties	— not applicable
consistency	Concurrent requests give the same output as the same requests run alone	✅ pass ²
spec	Speculative decoding on gives exactly the output of speculative decoding off	✅ pass ²
agent	Multi-turn tool-calling loop: exact prompt rendering, history reuse, valid tool calls, output vs llama.cpp	✅ pass ²
agent_think_off	The tool-calling loop with thinking off	— not applicable
sampling	temperature, top_k and seed behave as specified; first-token distribution matches llama.cpp	✅ pass ²
needle	Needle-in-a-haystack prompt at up to half the context, compared with llama.cpp; over-capacity prompt refused	✅ pass ³
needle_full	Needle-in-a-haystack prompt at the full context is served	✅ pass ²
decision	Decision answers (choice, score, noul probabilities) vs llama.cpp on the same prompts	— not applicable

• ¹ DevLab Dispatch run 42342192972, 2026-11-07; device AMD RYZEN AI MAX+ 395 w/ Radeon 8060S, AMD Radeon 8060S Graphics, Ubuntu 24.04; hrx-loom 0.1.0 (hrx-system d01d1c1).
• ² DevLab Dispatch run 42419939771, LEVEL=full, 2026-11-08; device AMD RYZEN AI MAX+ 395 w/ Radeon 8060S, AMD Radeon 8060S Graphics, Ubuntu 24.04; hrx-loom 0.1.0 (hrx-system d01d1c1).
• ³ DevLab Dispatch run 42342192972, LEVEL=full, 2026-11-07; device AMD RYZEN AI MAX+ 395 w/ Radeon 8060S, AMD Radeon 8060S Graphics, Ubuntu 24.04; hrx-loom 0.1.0 (hrx-system d01d1c1).
• Gates are the loom-benchmark correctness gates (gates.sh); a gate without a recorded run shows no evidence.

Files

• model.json / model.json.lock: the card and the pinned source files (sha256 checked by loom-fetch).
• program/*.loom, weights.loom: the generated Loom programs.
• tokenizer.json: the tokenizer.
• llm_config.json: the configuration the programs were generated from.
• info.json: the measurements, gate evidence and versions this card shows; everything else comes from the files above.
• REPORT.md: how the values above were measured: setup, commands and the value of every run.

Versions: card schema loom-card/2; loom-skills ca6ae88; hrx-loom 0.1.0 (hrx-system d01d1c1); card generator fc6b3b1.

Source files, fetched by loom-fetch from their own repositories:

• checkpoint: bartowski/Qwen2.5-7B-Instruct-GGUF @ 8911e8a, Qwen2.5-7B-Instruct-Q4_K_M.gguf