[bong-water-water-bong]

Moves third_party/llama.cpp from 706ad1a to 83e1c41, the merge of 1bit-MONSTER/llama.cpp#8 and the head of 1bit/hrx-vulkan-patched.

What the pin adds. Four architectures upstream llama.cpp has no model for (for gptj it has only a name), plus sibling spellings of HF classes the runtime already has. I checked each new port against transformers FP32 before merging #8 (teacher-forced top-1, 3 prompts × 32 tokens, one prompt of 340 tokens). Four of them needed fixes first:

model	agreement	fixed on the way
facebook/opt-125m	96/96	tokenizer checksum
EleutherAI/gpt-neo-125m	96/96	no attention scaling, as transformers does
Salesforce/codegen-350M-mono	96/96 (was 0/96)	the (query, value, key) blocks of qkv_proj; the lm_head bias
tiny-random-GPTJForCausalLM	89/89	lm_head bias, mask buffers, tokenizer checksum

I also dropped the BertForCausalLM/BertLMHeadModel aliases: those are causal decoders, and the bert arch is a bidirectional encoder.

Engine changes.

• serve: fork_only_arch now covers opt, codegen, gptneo and gptj next to zaya. Checked with the upstream Vulkan server present: OPT-125m and GPT-Neo-125m both answer through 1bit serve --device vulkan, served by our build.
• registry_build: runtime_archs now counts an architecture only when llama_model_mapping builds a model for it. Upstream's name-only gptj was being counted. FORK_ONLY matches serve's list.
• registry/architectures.json: 269 → 321 HF architectures mapped. The gap check passes.

🤖 Generated with Claude Code

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Commit 3a8290f

[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

🎫 Ticket compliance analysis 🔶 8 - Partially compliant Compliant requirements: Pin llama.cpp to commit 83e1c41 Add support for OPT, CodeGen, GPT-Neo, and GPT-J architectures Update registry_build.py to reflect new architectures Update architectures.json to include new architectures Ensure fork_only_arch function in serve.cpp includes new architectures Non-compliant requirements: None Requires further human verification: None

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Architecture List Synchronization The fork_only_arch function in app/serve.cpp is updated to include new architectures (opt, codegen, gptneo, gptj) to match the FORK_ONLY set in tools/registry_build.py. This change ensures that the architecture list is synchronized across the codebase. However, it's important to verify that all new architectures are correctly handled by the runtime and that no unintended architectures are included. bool fork_only_arch(const std::string& gguf) { // Zyphra ZAYA1 (docs/vulkan.md); OPT, CodeGen, GPT-Neo and GPT-J (llama.cpp #8: upstream has // no model for them, gptj only a name). Keep in step with FORK_ONLY in tools/registry_build.py. static const char* const archs[] = {"zaya", "opt", "codegen", "gptneo", "gptj"}; const std::string arch = gguf_architecture(gguf); for (const char* a : archs) if (arch == a) return true; return false; } Runtime Architecture Detection Logic The runtime_archs function in tools/registry_build.py has been updated to detect architectures that the runtime can build a model for by parsing llama-arch.cpp and llama-model.cpp. This change ensures that the registry accurately reflects which architectures are supported by the runtime. It's important to verify that the parsing logic correctly identifies all architectures that are actually supported. def runtime_archs(tree): """GGUF architecture names the runtime can build a model for: named in LLM_ARCH_NAMES and given a class in llama_model_mapping (a name alone, as upstream has for gptj, loads nothing).""" src = open(os.path.join(tree, "src/llama-arch.cpp")).read() body = src[src.index("LLM_ARCH_NAMES"):] body = body[:body.index("};")] names = dict(re.findall(r"\{\s*(LLM_ARCH_[A-Z0-9_]+)\s*,\s*\"([^\"]+)\"\s*\}", body)) model = open(os.path.join(tree, "src/llama-model.cpp")).read() start = model.index("llama_model_mapping(") mapping = model[start:model.index("\n}\n", start)] built = set(re.findall(r"case\s+(LLM_ARCH_[A-Z0-9_]+)\s*:", mapping)) return {n for e, n in names.items() if e in built} - {"clip"}