jukofyork/GLM-4.5-DRAFT-0.6B-v3.0

A 0.6B parameter draft (speculative decoding) model for use with GLM-4.5, GLM-4.5-Air and GLM-4-32B-0414.

See GLM-4.5-DRAFT-0.6B-v3.0-GGUF for the models in gguf format for use with llama.cpp.

---

Extending the context above 32k

The current config.json is set for context length up to 32k tokens. Add the "rope_scaling" section to config.json to enable YaRN, eg:

To extend the context to 64k:

  "max_position_embeddings": 65536,
  ...
  "rope_scaling": {
    "factor": 2.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  },

To extend the context to 128k:

  "max_position_embeddings": 131072,
  ...
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  },

NOTE: Because llama.cpp uses "static-YaRN" the scaling factor remains constant regardless of input length! Only add the rope_scaling configuration when processing long contexts is required...

---

How this model was created

1. The initial model was created from Qwen2.5-0.5B-Instruct using transplant-vocab:

> python ./transplant_vocab.py \
    ./Qwen2.5-0.5B-Instruct \
    ./GLM-4.5 \
    ./GLM-4.5-DRAFT-0.6B-UNTRAINED \
    --override "<|endoftext|>" "<|im_end|>" \
    --override "[MASK]" "<|endoftext|>" \
    --override "[gMASK]" "<|endoftext|>" \
    --override "[sMASK]" "<|endoftext|>" \
    --override "<sop>" "<|endoftext|>" \
    --override "<eop>" "<|endoftext|>" \
    --override "<|system|>" "<|im_start|>system" \
    --override "<|user|>" "<|im_end|><|im_start|>user" \
    --override "<|assistant|>" "<|im_start|>assistant" \
    --override "<|observation|>" "<|im_end|><|im_start|>user" \
    --override "<|code_prefix|>" "<|fim_prefix|>" \
    --override "<|code_middle|>" "<|fim_middle|>" \
    --override "<|code_suffix|>" "<|fim_suffix|>"

Loading config from 'Qwen2.5-0.5B-Instruct'... Done.
Loading config from 'GLM-4.5'... Done.
Loading tokenizer from 'Qwen2.5-0.5B-Instruct'... Done.
Loading tokenizer from 'GLM-4.5'... Done.
Loading model from 'Qwen2.5-0.5B-Instruct'... Done.

Input model configuration:
- Target vocabulary size    : 151552 (used = 151365, unused = 187)
- Donor vocabulary size     : 151936
- Donor num layers          : 24 (tied embeddings = True)
- Donor hidden size         : 896
- Donor attention heads     : 14
- Donor intermediate size   : 4864 (ratio = 1:5.4)
- Donor total parameters    : 494032768 (0.49B)
-- Embedding parameters     : 136134656 (0.14B)
-- Non-embedding parameters : 357898112 (0.36B)

Processing 3 automatic token overrides:
✘ 'bos_token_id' : Not found for target model
✔ 'eos_token_id' : 151329 '<|endoftext|>' → [151645] '<|im_end|>'
✘ 'pad_token_id' : 151329 is already mapped to [151645]

Processing 13 manual token overrides:
✔ 151329 : '<|endoftext|>' → [151645] '<|im_end|>'
✔ 151330 : '[MASK]' → [151643] '<|endoftext|>'
✔ 151331 : '[gMASK]' → [151643] '<|endoftext|>'
✔ 151332 : '[sMASK]' → [151643] '<|endoftext|>'
✔ 151333 : '<sop>' → [151643] '<|endoftext|>'
✔ 151334 : '<eop>' → [151643] '<|endoftext|>'
✔ 151335 : '<|system|>' → [151644, 8948] '<|im_start|>system'
✔ 151336 : '<|user|>' → [151645, 151644, 872] '<|im_end|><|im_start|>user'
✔ 151337 : '<|assistant|>' → [151644, 77091] '<|im_start|>assistant'
✔ 151338 : '<|observation|>' → [151645, 151644, 872] '<|im_end|><|im_start|>user'
✔ 151347 : '<|code_prefix|>' → [151659] '<|fim_prefix|>'
✔ 151348 : '<|code_middle|>' → [151660] '<|fim_middle|>'
✔ 151349 : '<|code_suffix|>' → [151661] '<|fim_suffix|>'

NOTE: Using an "untied" copy of 'embed_tokens.weight' as new 'lm_head.weight' tensor...

Transplanting tokens: 100%|████████████████████████████████████████████████████████████| 151365/151365 [00:44<00:00, 3376.18token/s]

Transplant mappings:
- 1 to 1  : 123103 (81%)
- 2 to 1  : 23942 (16%)
- 3 to 1  : 3264 (2.2%)
- 4 to 1  : 823 (0.54%)
- 5 to 1  : 181 (0.12%)
- 6 to 1  : 23 (0.015%)
- 7 to 1  : 21 (0.014%)
- 8 to 1  : 5 (0.0033%)
- 9 to 1  : 1 (0.00066%)
- 13 to 1 : 1 (0.00066%)
- 16 to 1 : 1 (0.00066%)

Head initialized with:
- Copies : 123103 (81%)
- Means  : 28262 (19%)
- Zeros  : 187 (0.12%)

Output model configuration:
- Output vocabulary size    : 151552
- Output num layers         : 24 (tied embeddings = False)
- Output hidden size        : 896
- Output attention heads    : 14
- Output intermediate size  : 4864 (ratio = 1:5.4)
- Output total parameters   : 629479296 (0.63B)
-- Embedding parameters     : 271581184 (0.27B)
-- Non-embedding parameters : 357898112 (0.36B)

Saving model and tokenizer to '/mnt/raid_drive/draft_finetunes/models/GLM-4.5-DRAFT-0.6B-UNTRAINED' folder
[2025-08-07 15:37:42,208] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)

Patching 'torch_dtype' in '/mnt/raid_drive/draft_finetunes/models/GLM-4.5-DRAFT-0.6B-UNTRAINED/config.json' based on actual saved tensors
- Updated 'torch_dtype' to 'bfloat16' based on actual tensor dtype

Operation completed successfully (ignore any 'segmentation fault' that follows!!!)

NOTE: After this, I had to manually add "bos_token_id": 151331 to config.json to get it to match the tokeniser when used in llama.cpp as a draft model.

2. The following datasets were used to create a fine-tuning dataset of ~2.3B tokens:

• agentlans/common-crawl-sample
• bigcode/the-stack-smol-xl
• rombodawg/Everything_Instruct (NOTE: output field only)

formatted just between <|endoftext|> tags.

3. The model was then trained using qlora-pipe-lite for 1 epoch with a batch size of 60 and a sequence length of 32k (~2M tokens per step):

# ==============================
# MODEL AND OUTPUT CONFIGURATION
# ==============================

model_dir = 'models/GLM-4.5-DRAFT-0.6B-UNTRAINED'
output_dir = 'finetuned'

# ===========================
# TRAINING TYPE CONFIGURATION
# ===========================

full_fine_tune = true

# =======================
# OPTIMIZER CONFIGURATION
# =======================

lr = 5e-5

# ======================
# TRAINING CONFIGURATION
# ======================

sequence_len = 32768

gradient_accumulation_steps = 10  # 10×6 = batch size 60, 10×6×32768 = ~2M tokens per step

# =====================
# DATASET CONFIGURATION
# =====================

[[datasets]]
dataset_path = 'datasets/common-crawl-sample/*.json'
drop_tails = true

[[datasets]]
dataset_path = 'datasets/the-stack-smol-xl/*.jsonl'
drop_tails = true

[[datasets]]
dataset_path = 'datasets/rombodawg-Everything-Instruct/*.json'
drop_tails = true

I used six RTX A6000 GPUs over three nodes and hence the 60 batch size (6 x 10 gradient accumulation steps = 60).