GLM-4-9B-0414 Neon v2
RP finetune of GLM-4-9B-0414. Feels nice, lots of personality, if bit quirky sometimes. Nice prose, not too Claude-ish or Gemini-ish. Doesn't seem to like too long system prompts or charcards though. Seems to like JSON formatted system prompts.
Model was trained by Auri.
Training notes
Model was trained on a dataset consisting of 77M tokens of synthetic RP and short story gen data for one epoch. Training took around 11 hours on 2xRTX 3090 workstation, generously provided by OwenArli. Went with some sane defaults for training config, QLoRA plus CCE for a nice chunk of memory usage optimization, 16k fit on 48GB nicely with some room to spare. I seem to have a problem with Eval/Loss being broken, not sure why, otherwise it trained smoothly.
Huge thanks to ArliAI for providing compute and collaborating on this run!
Format
Model responds to GLM4 instruct formatting, exactly like it's base model. Backends struggle to add BOS token automatically, so you'll need to do it yourself. Jinja template should work for chat completions.
[gMASK]<sop><|system|>
{system_prompt}<|user|>
{prompt}<|assistant|>
Recommended Samplers
Nothing special, just classics.
Temperature - 1
Min-P - 0.1
Repetition Penalty - 1.03
Example master import for SillyTavern (using Shingane-v1 system prompt by Steelskull)
Running on KoboldCPP and other backends
To run GGUFs correctly, you need the most recent version of KoboldCPP, and to pass --overridekv glm4.rope.dimension_count=int:64 to the CLI command or put glm4.rope.dimension_count=int:64 into overridekv box in the GUI (under the Tokens tab at the very bottom).
Thanks to DaringDuck and tofumagnate for info how to apply this fix.
To run this model on vLLM, you'll need to build it from source from the git repo, full GLM4 support hasn't reached release yet.
ExLLaMAv2 and v3 based backends, such as TabbyAPI should support the model out of the box.
Latest versions of llama.cpp server should also allow running GGUFs out-of-the-box.
Special Thanks
Once again, huge kudos to OwenArli for providing compute and helping with tuning along the way!
Big thanks to Artus for providing free inference for pre-release showcase of this model!
And big thanks to BeaverAI community for giving feedback and helping to figure out optimal settings!
Training config
See Axolotl config# Model
base_model: /home/owen/models/GLM-4-9B-0414
strict: false
model_type: AutoModelForCausalLM
# Liger Kernels and CCE (optimization)
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: false
liger_rms_norm: false
liger_glu_activation: false
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
# Output and HuggingFace
output_dir: ./GLM-9B-Neon-v2
hub_model_id: AuriAetherwiing/GLM-9B-Neon-v2-LoRA
hf_use_auth_token: true
hub_strategy: "all_checkpoints"
# WandB
wandb_project: allura-org
wandb_entity:
wandb_name: GLM-9B-Neon-v2
# === Data Configuration ===
# Data
#chat_template: chatml
#train_on_inputs: false
group_by_length: false
datasets:
- path: ./Neon/neon.jsonl
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
- path: ./Neon/S2.jsonl
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
- path: ./Neon/SystemChat_subset_filtered_sharegpt_utf8fix.jsonl
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
dataset_prepared_path: ./lora_last_run_prepared
## Evaluation
val_set_size: 0.01
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
# Technical aspects
sequence_len: 16384
save_safetensors: true
saves_per_epoch: 2
logging_steps: 1
#special_tokens:
# pad_token: <pad>
# Quantization
bf16: auto
fp16:
tf32: false
## For LoRA
load_in_8bit: false
load_in_4bit: true
# LoRA
peft_use_rslora: false
peft_use_dora: false # better but slower
adapter: qlora # lora or qlora
lora_model_dir:
lora_r: 64 # 64 is optimal for most trains on instruct
lora_alpha: 64
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
# loraplus_lr_ratio: 8 # works to converge faster but is kinda cancer bc makes model unstable
#loraplus_lr_embedding:
# Training hyperparameters
# max_steps:
num_epochs: 1
# Anti Overfit and Stability
weight_decay: 0.01
max_grad_norm: 1.0
## Learning Rate
warmup_ratio: 0.05
learning_rate: 1e-5
lr_scheduler: rex
#lr_scheduler_kwargs:
# min_lr: 0.0000024
optimizer: adamw_torch # usually adamw_torch or paged_adamw_8bit
## Batch Size
gradient_accumulation_steps: 32 # More effective batch size - stabler train, usually. MBS also speeds it up.
micro_batch_size: 1 # Batch size per gpu = micro_batch_size * gradient_accumulation_steps
eval_batch_size: 1
# Optimizations
pad_to_sequence_len: true
sample_packing: true
eval_sample_packing: false
flash_attention: true
xformers_attention:
gradient_checkpointing:
gradient_checkpointing_kwargs:
use_reentrant: false
# Set to a divisor (> 1) of the number of GPUs available
#sequence_parallel_degree: 2 # Split sequences across 4 GPUs
# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
#heads_k_stride: 1
# Optional; one of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to
# "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
#ring_attn_func:
# deepspeed: /home/owen/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_all.json
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: Glm4DecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
fsdp_activation_checkpointing: true