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Model: CMSManhattan/JiRackUltra_1b Source: Original Platform
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ModelHub XC
371
jirack_to_gguf_1p5b.py
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371
jirack_to_gguf_1p5b.py
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# ==============================================================================
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# JiRack -> GGUF converter, 1.5B edition (stage 1: .pt -> HuggingFace folder)
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# COPYRIGHT (c) 2026 Konstantin Vladimirovich Grabko.
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#
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# Verified against JiRackTernaryUltra_1b.py [DS1.5-1]:
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# vocab_size 151936, hidden 1536, n_layers 28, n_heads 12, n_kv_heads 2,
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# head_dim 128 (12*128=1536 -- the converter's hardcoded 128 is correct),
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# rope_theta 10000.0 (same as 7B), rms_eps 1e-6,
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# tie_word_embeddings = FALSE [DS1.5-2] -- lm_head ships separately, and
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# this converter auto-detects that from the presence of lm_head.weight.
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#
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# Pipeline is two stages:
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#
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# Stage 1 (THIS SCRIPT, run in venv_ji):
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# model.pt -> HF folder (model.safetensors + config.json + tokenizer)
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#
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# Stage 2 (llama.cpp, run once per model):
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# python convert_hf_to_gguf.py <hf_folder> \
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# --outfile jirack_1p5b.gguf --outtype bf16
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# ./build/bin/llama-quantize jirack_1p5b.gguf \
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# jirack_1p5b.Q4_K_M.gguf Q4_K_M
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#
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# Key points handled here:
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# * config.json is derived from the ACTUAL tensor shapes in the checkpoint,
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# so vocab (151936 vs 7B's 152064) and any Net2Net-expanded FFN width are
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# picked up automatically -- no stock-config copying.
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# * lambda_ buffers (ternary fake-quant training machinery) are dropped --
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# at inference you run set_lambda(0.0) anyway, so the stored weights ARE
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# the full-precision weights; the exported model is a plain Qwen2 dense.
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# * Keys: HF naming passes through; JiRack native naming
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# (token_emb / blocks.N.* / ffn_w1-w3-w2) is remapped automatically.
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#
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# EDIT THE THREE PATHS BELOW.
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# ==============================================================================
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import json
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import os
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import re
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import shutil
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import sys
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import torch
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# ========================= EDIT THESE =========================
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CKPT_PATH = "model.pt"
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TOKENIZER_DIR = "."
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OUTPUT_DIR = "."
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# rope_theta cannot be inferred from tensor shapes -- set per base model:
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# DeepSeek-R1-Distill-Qwen-1.5B -> 10000.0 (same as 7B)
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# DeepSeek-R1-Distill-Qwen-14B -> 1000000.0
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# DeepSeek-R1-Distill-Qwen-32B -> 1000000.0
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ROPE_THETA = 10000.0
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MAX_POSITION = 131072
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RMS_NORM_EPS = 1e-6
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# Q2_0 = 2-bit ternary {-1, 0, +1} quantization, one fp16 scale per group of
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# weights -- the real encoding for BitNet-style ternary weights, once inference
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# actually runs true ternary rather than bf16 dense. For now Q4_K_M remains
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# the practical choice; the Q2_0 command is just printed ready for later.
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EMIT_Q2_0_CMD = True
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Q2_0_GROUP = 64 # 64 = mainline llama.cpp, no fork needed.
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# ================================================================
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# HF Qwen2 key patterns we expect to find (N = layer index)
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HF_LAYER_KEYS = [
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"model.layers.{n}.self_attn.q_proj.weight",
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"model.layers.{n}.self_attn.q_proj.bias",
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"model.layers.{n}.self_attn.k_proj.weight",
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"model.layers.{n}.self_attn.k_proj.bias",
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"model.layers.{n}.self_attn.v_proj.weight",
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"model.layers.{n}.self_attn.v_proj.bias",
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"model.layers.{n}.self_attn.o_proj.weight",
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"model.layers.{n}.mlp.gate_proj.weight",
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"model.layers.{n}.mlp.up_proj.weight",
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"model.layers.{n}.mlp.down_proj.weight",
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"model.layers.{n}.input_layernorm.weight",
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"model.layers.{n}.post_attention_layernorm.weight",
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]
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HF_TOP_KEYS = [
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"model.embed_tokens.weight",
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"model.norm.weight",
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"lm_head.weight",
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]
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def load_state_dict(path):
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print(f"📥 Loading checkpoint: {path}")
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ckpt = torch.load(path, map_location="cpu", weights_only=False)
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sd = ckpt["model"] if isinstance(ckpt, dict) and "model" in ckpt else ckpt
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if not isinstance(sd, dict):
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sys.exit("❌ Checkpoint is not a state_dict and has no 'model' key.")
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return sd
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def drop_training_buffers(sd):
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dropped = [k for k in sd if k.endswith("lambda_")]
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for k in dropped:
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del sd[k]
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if dropped:
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print(f"🧹 Dropped {len(dropped)} lambda_ buffers (ternary training machinery).")
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return sd
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def normalize_keys(sd):
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"""Pass HF-style keys through; try trivial prefix fixes; else abort with a listing."""
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keys = list(sd.keys())
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# Case 1: already HF-style
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if "model.embed_tokens.weight" in sd:
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print("✅ Keys already use HF (Qwen2) naming -- no remap needed.")
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return sd
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# Case 2: same names but without the leading 'model.' (e.g. 'embed_tokens.weight')
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if "embed_tokens.weight" in sd:
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print("🔁 Keys look HF-like without the 'model.' prefix -- adding it.")
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out = {}
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for k, v in sd.items():
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if k == "lm_head.weight":
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out[k] = v
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else:
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out["model." + k] = v
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if "model.embed_tokens.weight" in out:
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return out
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# Case 3: JiRack native naming (token_emb / blocks.N.* / ffn_w1-w3-w2)
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if "token_emb.weight" in sd and any(k.startswith("blocks.") for k in sd):
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print("🔁 JiRack native naming detected -- remapping to HF (Qwen2) keys.")
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hidden = sd["token_emb.weight"].shape[1]
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block_map = {
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"norm1.weight": "input_layernorm.weight",
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"norm2.weight": "post_attention_layernorm.weight",
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"q_proj.weight": "self_attn.q_proj.weight",
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"q_proj.bias": "self_attn.q_proj.bias",
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"k_proj.weight": "self_attn.k_proj.weight",
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"k_proj.bias": "self_attn.k_proj.bias",
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"v_proj.weight": "self_attn.v_proj.weight",
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"v_proj.bias": "self_attn.v_proj.bias",
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"out_proj.weight": "self_attn.o_proj.weight",
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"ffn_w1.weight": "mlp.gate_proj.weight", # SwiGLU gate
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"ffn_w3.weight": "mlp.up_proj.weight", # SwiGLU up
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"ffn_w2.weight": "mlp.down_proj.weight", # SwiGLU down
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}
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out = {"model.embed_tokens.weight": sd["token_emb.weight"]}
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leftovers = {}
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blk_pat = re.compile(r"^blocks\.(\d+)\.(.+)$")
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for k, v in sd.items():
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if k == "token_emb.weight":
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continue
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m = blk_pat.match(k)
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if m:
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idx, sub = m.group(1), m.group(2)
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if sub == "out_proj.bias":
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sys.exit("❌ out_proj has a bias -- Qwen2 arch has no o_proj "
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"bias, this checkpoint isn't Qwen2-compatible as-is.")
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if sub not in block_map:
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sys.exit(f"❌ Unknown per-block key: {k} -- send this back.")
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out[f"model.layers.{idx}.{block_map[sub]}"] = v
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else:
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leftovers[k] = v
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# classify the remaining top-level keys by tensor shape
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for k, v in leftovers.items():
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shp = tuple(v.shape)
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if len(shp) == 1 and shp[0] == hidden:
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print(f" final norm : {k} -> model.norm.weight")
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out["model.norm.weight"] = v
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elif len(shp) == 2 and shp[1] == hidden:
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print(f" lm head : {k} -> lm_head.weight")
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out["lm_head.weight"] = v
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else:
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sys.exit(f"❌ Unexplained top-level key: {k} {shp} -- send back.")
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if "model.norm.weight" not in out:
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sys.exit("❌ No final-norm tensor found (1-D, size=hidden). Send the "
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"full key list (the tail beyond the first 80).")
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print(f"✅ Remapped {len(out)} tensors to HF naming.")
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return out
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# Case 4: unknown naming -- print everything and stop
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print("❌ Unrecognized key naming scheme. Full key list (first 80):")
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for k in keys[:80]:
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print(" ", k, tuple(sd[k].shape) if hasattr(sd[k], "shape") else "")
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print(f" ... total {len(keys)} keys")
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sys.exit(
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"\nSend this key list back and I'll add the exact JiRack->HF mapping "
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"to normalize_keys()."
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)
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def infer_config(sd):
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"""Derive Qwen2 config.json entirely from tensor shapes."""
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embed = sd["model.embed_tokens.weight"]
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vocab_size, hidden_size = embed.shape
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layer_ids = set()
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pat = re.compile(r"^model\.layers\.(\d+)\.")
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for k in sd:
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m = pat.match(k)
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if m:
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layer_ids.add(int(m.group(1)))
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num_layers = max(layer_ids) + 1
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q_w = sd["model.layers.0.self_attn.q_proj.weight"] # [n_heads*head_dim, hidden]
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k_w = sd["model.layers.0.self_attn.k_proj.weight"] # [n_kv*head_dim, hidden]
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gate = sd["model.layers.0.mlp.gate_proj.weight"] # [intermediate, hidden]
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intermediate_size = gate.shape[0]
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# Qwen2 1.5B/7B/14B/32B all use head_dim=128 (1.5B: 12*128=1536)
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head_dim = 128
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num_attention_heads = q_w.shape[0] // head_dim
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num_key_value_heads = k_w.shape[0] // head_dim
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# sanity: every layer's FFN must have the same (expanded) width
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widths = {sd[f"model.layers.{i}.mlp.gate_proj.weight"].shape[0] for i in layer_ids}
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if len(widths) != 1:
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sys.exit(f"❌ Inconsistent FFN widths across layers: {sorted(widths)}")
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tie = "lm_head.weight" not in sd
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cfg = {
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"architectures": ["Qwen2ForCausalLM"],
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"model_type": "qwen2",
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"vocab_size": vocab_size,
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"hidden_size": hidden_size,
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"intermediate_size": intermediate_size,
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"num_hidden_layers": num_layers,
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"num_attention_heads": num_attention_heads,
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"num_key_value_heads": num_key_value_heads,
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"hidden_act": "silu",
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"max_position_embeddings": MAX_POSITION,
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"rms_norm_eps": RMS_NORM_EPS,
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"rope_theta": ROPE_THETA,
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"tie_word_embeddings": tie,
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"torch_dtype": "bfloat16",
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"use_cache": True,
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"bos_token_id": 151646,
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"eos_token_id": 151643,
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}
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print("🧾 Inferred config from tensor shapes:")
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for k in ("vocab_size", "hidden_size", "intermediate_size", "num_hidden_layers",
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"num_attention_heads", "num_key_value_heads", "tie_word_embeddings"):
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print(f" {k} = {cfg[k]}")
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print(f" rope_theta = {ROPE_THETA} (from the EDIT block -- verify for this base model!)")
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return cfg
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def save_hf(sd, cfg):
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"🔄 Casting weights to bf16 on {device.upper()} ...")
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for k in sd:
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t = sd[k]
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if torch.is_tensor(t) and t.is_floating_point():
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sd[k] = t.to(device=device, dtype=torch.bfloat16).cpu().contiguous()
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try:
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from safetensors.torch import save_file
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# single-file safetensors; llama.cpp's converter handles it fine
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path = os.path.join(OUTPUT_DIR, "model.safetensors")
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print(f"💾 Saving {path} ...")
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save_file(sd, path, metadata={"format": "pt"})
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except ImportError:
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# fallback: pytorch_model.bin, also accepted by convert_hf_to_gguf.py
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path = os.path.join(OUTPUT_DIR, "pytorch_model.bin")
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print(f"⚠️ safetensors not installed -- saving {path} instead (also works).")
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torch.save(sd, path)
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with open(os.path.join(OUTPUT_DIR, "config.json"), "w") as f:
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json.dump(cfg, f, indent=2)
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with open(os.path.join(OUTPUT_DIR, "generation_config.json"), "w") as f:
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json.dump({"bos_token_id": cfg["bos_token_id"],
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"eos_token_id": cfg["eos_token_id"],
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"do_sample": True, "temperature": 0.6, "top_p": 0.95}, f, indent=2)
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print(f"📎 Copying tokenizer from {TOKENIZER_DIR} ...")
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same_dir = os.path.abspath(TOKENIZER_DIR) == os.path.abspath(OUTPUT_DIR)
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if same_dir:
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print(" TOKENIZER_DIR == OUTPUT_DIR -- tokenizer files are already in "
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"place, skipping copy.")
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copied = sum(
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1 for name in os.listdir(TOKENIZER_DIR)
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if name.startswith(("tokenizer", "special_tokens", "added_tokens",
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"vocab", "merges", "chat_template"))
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)
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else:
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copied = 0
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for name in os.listdir(TOKENIZER_DIR):
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if name.startswith(("tokenizer", "special_tokens", "added_tokens", "vocab", "merges", "chat_template")):
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shutil.copy2(os.path.join(TOKENIZER_DIR, name), os.path.join(OUTPUT_DIR, name))
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copied += 1
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if copied == 0:
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sys.exit(f"❌ No tokenizer files found in {TOKENIZER_DIR}")
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print(f" copied {copied} tokenizer files.")
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def verify(cfg):
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"""Cross-check tokenizer length vs embedding rows."""
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try:
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from transformers import AutoTokenizer
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tok = AutoTokenizer.from_pretrained(OUTPUT_DIR)
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n = len(tok)
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rows = cfg["vocab_size"]
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if n > rows:
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sys.exit(f"❌ Tokenizer has {n} tokens but embedding matrix only {rows} rows -- "
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f"resize the checkpoint before converting.")
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print(f"✅ Tokenizer check: {n} tokens <= {rows} embedding rows "
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f"({rows - n} spare rows).")
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except Exception as e:
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print(f"⚠️ Could not verify tokenizer ({e}) -- continuing anyway.")
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def main():
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if not os.path.exists(CKPT_PATH):
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sys.exit(f"❌ {CKPT_PATH} not found")
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sd = load_state_dict(CKPT_PATH)
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sd = drop_training_buffers(sd)
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sd = normalize_keys(sd)
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cfg = infer_config(sd)
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save_hf(sd, cfg)
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verify(cfg)
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print("\n" + "=" * 78)
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print("✅ Stage 1 done. HF model at:", OUTPUT_DIR)
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print("=" * 78)
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out_norm = OUTPUT_DIR.rstrip("/")
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gguf_base = "jirack_1p5b" if out_norm in ("", ".") else out_norm
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q2_0_block = ""
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if EMIT_Q2_0_CMD:
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suffix = "Q2_0" if Q2_0_GROUP == 64 else f"Q2_0_g{Q2_0_GROUP}"
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fork_note = (
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"group-64 is in mainline llama.cpp -- no fork needed, CPU/Metal ready."
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if Q2_0_GROUP == 64 else
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"group-128 needs a CUDA fork -- not needed on CPU-only."
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)
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q2_0_block = """
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Ternary quantization (Q2_0, 2 bits/weight, {{-1,0,+1}} + fp16 group scale --
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this is the real encoding for BitNet-style ternary weights, once your model
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actually runs true ternary at inference rather than bf16 dense):
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{fork_note}
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./build/bin/llama-quantize {gguf} {gguf_q2} {suffix}
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""".format(
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fork_note=fork_note,
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gguf=gguf_base + ".gguf",
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gguf_q2=gguf_base + f".{suffix}.gguf",
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suffix=suffix,
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)
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print("""
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Stage 2 -- make the GGUF (one-time llama.cpp setup, then per model):
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git clone https://github.com/ggml-org/llama.cpp /mnt/nfs_share/llama.cpp
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cd /mnt/nfs_share/llama.cpp
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pip install -r requirements.txt
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python convert_hf_to_gguf.py {out} \\
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--outfile {gguf} --outtype bf16
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Optional dense quantization (build llama.cpp first: cmake -B build && cmake --build build -j):
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./build/bin/llama-quantize {gguf} {gguf_q} Q4_K_M
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{q2_0_block}""".format(
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out=OUTPUT_DIR,
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gguf=gguf_base + ".gguf",
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gguf_q=gguf_base + ".Q4_K_M.gguf",
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q2_0_block=q2_0_block,
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))
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if __name__ == "__main__":
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main()
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