jukofyork / DeepSeek-V3-0324-CODER-DRAFT-0.6B-v1.1

NOTE : This is just a slightly improved version that I trained using "max_position_embeddings": 65536 + "rope_scaling": {"factor": 2.0, ... rather than setting the rope_scaling after training...

A 0.6B parameter draft (speculative decoding) model for use with deepseek-ai/DeepSeek-V3-0324 and deepseek-ai/DeepSeek-V3 .

NOTES :

• This version (unlike the previous jukofyork/DeepSeek-V3-0324-DRAFT-0.5B-v1.0 ), was trained using only the agentlans/common-crawl-sample and bigcode/the-stack-smol-xl datasets.
• This version (unlike the previous jukofyork/DeepSeek-V3-0324-DRAFT-0.5B-v1.0 ), doesn't trim the heads down from 14 to 12.

See jukofyork/DeepSeek-V3-0324-CODER-DRAFT-0.6B-v1.0-GGUF for the models in GGUF format.

How the model was created

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

python ./transplant_vocab.py \                                  
        Qwen2.5-Coder-0.5B-Instruct \
        DeepSeek-V3-0324-BF16 \
        DeepSeek-V3-0324-CODER-DRAFT-0.6B-UNTRAINED \
        --override "<｜▁pad▁｜>" "<|endoftext|>" \
        --override "<｜fim▁hole｜>" "<|fim_middle|>" \
        --override "<｜fim▁begin｜>" "<|fim_prefix|>" \
        --override "<｜fim▁end｜>" "<|fim_suffix|>" \
        --override "<｜User｜>" "<|im_start|>user\\n" \
        --override "<｜Assistant｜>" "<|im_start|>assistant\\n" \
        --override "<|EOT|>" "<|endoftext|>" \
        --override "<｜tool▁calls▁begin｜>" "<tool_call>" \
        --override "<｜tool▁call▁begin｜>" "<tool_call>" \
        --override "<｜tool▁outputs▁begin｜>" "<tool_call>" \
        --override "<｜tool▁output▁begin｜>" "<tool_call>" \
        --override "<｜tool▁calls▁end｜>" "</tool_call>" \
        --override "<｜tool▁call▁end｜>" "</tool_call>" \
        --override "<｜tool▁outputs▁end｜>" "</tool_call>" \
        --override "<｜tool▁output▁end｜>" "</tool_call>" \
        --override "<｜tool▁sep｜>" "</tool_call>"

2. The following datasets were merged to create a fine-tuning dataset of ~2.5B tokens:

• agentlans/common-crawl-sample
• bigcode/the-stack-smol-xl

formatted just between <｜end▁of▁sentence｜> tags.

3. The model was then trained using qlora-pipe for 1 epoch with a batch size of 120 and a sequence length of 32k (~4M tokens per step):

# Resume a prior run
resume_from_checkpoint = false

# Paths
model = 'DeepSeek-V3-0324-CODER-DRAFT-0.6B-UNTRAINED'
output_dir = 'DeepSeek-V3-0324-CODER-DRAFT-0.6B'

# Optimization configuration
full_fine_tune = true
epochs = 1
lr_scheduler = 'cosine'
warmup_steps = 100

# Performance settings
pipeline_stages = 1
logging_steps = 1
eval_steps = 100
save_steps = 100
checkpoint_every_n_minutes = 60
eval_before_first_step = true
eval_after_last_step = true
model_weight_dtype = 'bfloat16'
keep_states = 3
group_by_length = true
activation_checkpointing = 'unsloth'

# Dataset configuration
dataset_combination_mode = 'concatenate'
eval_gradient_accumulation_steps = 20

[optimizer]
type = 'adamw_kahan'
lr = 5e-5
beta1 = 0.9
beta2 = 0.999
weight_decay = 0.01

[[datasets]]
name = 'mixed_data'
dataset_type = 'textfile'
dataset_path = 'mixed_data/*.txt'
sequence_len = 32768
eval_size = 0.01

{
    "train_micro_batch_size_per_gpu": 1,
    "gradient_accumulation_steps": 20,
    "gradient_clipping": 1.0,
    "steps_per_print": 1
}

I used six RTX A6000 GPUs over three nodes and hence the 120 batch size ( 6 x 20 gradient accumulation steps = 120 ).