---
library_name: transformers
license: apache-2.0
base_model: EVA-UNIT-01/EVA-Qwen2.5-32B-v0.0
tags:
- generated_from_trainer
model-index:
- name: EVA-Qwen2.5-32B-SFFT-v0.0
  results: []
quantized_by: SvdH
base_model_relation: quantized
---

# EVA Qwen2.5-32B v0.0

6BPW ExLLamaV2 quant of https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-32B-v0.0

Using parquet: https://huggingface.co/datasets/roleplay4fun/pippa

<p>
  A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-32B on mixture of synthetic and natural data.<br>
  It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model.<br>
</p>

<p>Note: using quantized KV cache with Qwen2.5 <b>is not recommended</b> and can lead to degraded output quality. On the other hand, Qwen's KV cache is already light enough, so using f16 for it shouldn't be problematic.</p>

<p>
  <p>Prompt format is ChatML.</p><br>
  <h3>Recommended sampler values:</h3>
  <ul>
  <li>Temperature: 1</li>
  <li>Typical-P: 0.9</li>
  <li>Min-P: 0.05</li>
  <li>Top-A: 0.2</li>
  <li>Repetition Penalty: 1.03</li>
  </ul>
  
  <h3>Recommended SillyTavern presets (via CalamitousFelicitousness):</h3>

  - [Context](https://huggingface.co/EVA-UNIT-01/EVA-Yi-1.5-9B-32K-V1/blob/main/%5BChatML%5D%20Roleplay-v1.9%20Context.json)
  - [Instruct and System Prompt](https://huggingface.co/EVA-UNIT-01/EVA-Yi-1.5-9B-32K-V1/blob/main/%5BChatML%5D%20Roleplay-v1.9%20Instruct.json)
</p>

<p>
  <br>
  <h3>
    Training data:
  </h3>
    <ul>
      <li>Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's <a href=https://huggingface.co/nothingiisreal/L3.1-70B-Celeste-V0.1-BF16>card</a> for details.</li>
      <li>Kalomaze's Opus_Instruct_25k dataset, filtered for refusals.</li>
      <li>A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe</li>
      <li>A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe</li>
      <li>Synthstruct and SynthRP datasets by Epiculous</li>
    </ul>
  <h3>
     Training time and hardware:
  </h3>
      <ul><li>7 hours on 8xH100 SXM, provided by <a href=https://featherless.ai/>FeatherlessAI</a></li></ul><br>
</p>
  <p>Model was trained by Kearm and Auri.</p>
  <h4>Special thanks:</h4><ul>
  <li><b>to <a href=https://featherless.ai/>FeatherlessAI</a> for generously providing 8xH100 SXM node for training of this model</b></li>
  <li>to Gryphe, Lemmy, Kalomaze, Nopm and Epiculous for the data</li>
  <li>and to Allura-org for support and feedback on EVA models.</li></ul>

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: Qwen/Qwen2.5-32B

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

# plugins:
#   - axolotl.integrations.spectrum.SpectrumPlugin

# spectrum_top_fraction: 0.5
# # Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
# spectrum_model_name: Qwen/Qwen2.5-32B

datasets:
  - path: datasets/deduped_Synthstruct-Gens_processed_sharegpt_converted_cleaned.jsonl
    type: sharegpt
  - path: datasets/opus-instruct-22k-no_refusals-filtered.jsonl
    type: sharegpt
  - path: datasets/Celeste_Filtered.jsonl
    type: sharegpt
  - path: datasets/Gryphe-S3-5-Charcards-names-2k.jsonl
    type: sharegpt
  - path: datasets/deduped_SynthRP-Gens_processed_09-25-2024-ShareGPT_converted_cleaned.jsonl
    type: sharegpt
  - path: datasets/deduped_Gryphe-4o-WP-1k.jsonl
    type: sharegpt
  - path: datasets/deduped_not_samantha_norefusals.jsonl
    type: sharegpt

chat_template: chatml
shuffle_merged_datasets: true
val_set_size: 0.001
output_dir: ./EVA-Qwen2.5-32B-SFFT-v0.0

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

# adapter: qlora
# lora_model_dir:
# lora_r: 64
# lora_alpha: 64
# lora_dropout: 0.05
# lora_target_linear: true
# peft_use_dora: true

unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
# input_layernorm layers
- model.layers.0.input_layernorm
- model.layers.1.input_layernorm
- model.layers.2.input_layernorm
- model.layers.3.input_layernorm
- model.layers.4.input_layernorm
- model.layers.5.input_layernorm
- model.layers.6.input_layernorm
- model.layers.7.input_layernorm
- model.layers.8.input_layernorm
- model.layers.9.input_layernorm
- model.layers.10.input_layernorm
- model.layers.11.input_layernorm
- model.layers.12.input_layernorm
- model.layers.13.input_layernorm
- model.layers.14.input_layernorm
- model.layers.15.input_layernorm
- model.layers.16.input_layernorm
- model.layers.17.input_layernorm
- model.layers.18.input_layernorm
- model.layers.19.input_layernorm
- model.layers.20.input_layernorm
- model.layers.21.input_layernorm
- model.layers.22.input_layernorm
- model.layers.23.input_layernorm
- model.layers.24.input_layernorm
- model.layers.25.input_layernorm
- model.layers.26.input_layernorm
- model.layers.27.input_layernorm
- model.layers.28.input_layernorm
- model.layers.29.input_layernorm
- model.layers.30.input_layernorm
- model.layers.31.input_layernorm
# lm_head layers
# mlp.down_proj layers
- model.layers.63.mlp.down_proj
- model.layers.49.mlp.down_proj
- model.layers.48.mlp.down_proj
- model.layers.45.mlp.down_proj
- model.layers.44.mlp.down_proj
- model.layers.47.mlp.down_proj
- model.layers.46.mlp.down_proj
- model.layers.43.mlp.down_proj
- model.layers.8.mlp.down_proj
- model.layers.11.mlp.down_proj
- model.layers.19.mlp.down_proj
- model.layers.35.mlp.down_proj
- model.layers.20.mlp.down_proj
- model.layers.52.mlp.down_proj
- model.layers.39.mlp.down_proj
- model.layers.62.mlp.down_proj
- model.layers.50.mlp.down_proj
- model.layers.29.mlp.down_proj
- model.layers.16.mlp.down_proj
- model.layers.28.mlp.down_proj
- model.layers.53.mlp.down_proj
- model.layers.30.mlp.down_proj
- model.layers.31.mlp.down_proj
- model.layers.32.mlp.down_proj
- model.layers.7.mlp.down_proj
- model.layers.36.mlp.down_proj
- model.layers.12.mlp.down_proj
- model.layers.18.mlp.down_proj
- model.layers.37.mlp.down_proj
- model.layers.38.mlp.down_proj
- model.layers.14.mlp.down_proj
- model.layers.13.mlp.down_proj
# mlp.gate_proj layers
- model.layers.43.mlp.gate_proj
- model.layers.61.mlp.gate_proj
- model.layers.60.mlp.gate_proj
- model.layers.44.mlp.gate_proj
- model.layers.62.mlp.gate_proj
- model.layers.28.mlp.gate_proj
- model.layers.29.mlp.gate_proj
- model.layers.45.mlp.gate_proj
- model.layers.37.mlp.gate_proj
- model.layers.35.mlp.gate_proj
- model.layers.59.mlp.gate_proj
- model.layers.36.mlp.gate_proj
- model.layers.30.mlp.gate_proj
- model.layers.48.mlp.gate_proj
- model.layers.38.mlp.gate_proj
- model.layers.27.mlp.gate_proj
- model.layers.31.mlp.gate_proj
- model.layers.39.mlp.gate_proj
- model.layers.34.mlp.gate_proj
- model.layers.58.mlp.gate_proj
- model.layers.33.mlp.gate_proj
- model.layers.26.mlp.gate_proj
- model.layers.32.mlp.gate_proj
- model.layers.46.mlp.gate_proj
- model.layers.42.mlp.gate_proj
- model.layers.49.mlp.gate_proj
- model.layers.57.mlp.gate_proj
- model.layers.50.mlp.gate_proj
- model.layers.47.mlp.gate_proj
- model.layers.56.mlp.gate_proj
- model.layers.63.mlp.gate_proj
- model.layers.55.mlp.gate_proj
# mlp.up_proj layers
- model.layers.61.mlp.up_proj
- model.layers.60.mlp.up_proj
- model.layers.32.mlp.up_proj
- model.layers.59.mlp.up_proj
- model.layers.58.mlp.up_proj
- model.layers.57.mlp.up_proj
- model.layers.44.mlp.up_proj
- model.layers.28.mlp.up_proj
- model.layers.35.mlp.up_proj
- model.layers.36.mlp.up_proj
- model.layers.31.mlp.up_proj
- model.layers.34.mlp.up_proj
- model.layers.55.mlp.up_proj
- model.layers.29.mlp.up_proj
- model.layers.49.mlp.up_proj
- model.layers.30.mlp.up_proj
- model.layers.53.mlp.up_proj
- model.layers.43.mlp.up_proj
- model.layers.56.mlp.up_proj
- model.layers.33.mlp.up_proj
- model.layers.54.mlp.up_proj
- model.layers.62.mlp.up_proj
- model.layers.27.mlp.up_proj
- model.layers.51.mlp.up_proj
- model.layers.52.mlp.up_proj
- model.layers.37.mlp.up_proj
- model.layers.45.mlp.up_proj
- model.layers.26.mlp.up_proj
- model.layers.42.mlp.up_proj
- model.layers.50.mlp.up_proj
- model.layers.48.mlp.up_proj
- model.layers.39.mlp.up_proj
# model.embed_tokens layers
# model.norm layers
# post_attention_layernorm layers
- model.layers.0.post_attention_layernorm
- model.layers.1.post_attention_layernorm
- model.layers.2.post_attention_layernorm
- model.layers.3.post_attention_layernorm
- model.layers.4.post_attention_layernorm
- model.layers.5.post_attention_layernorm
- model.layers.6.post_attention_layernorm
- model.layers.7.post_attention_layernorm
- model.layers.8.post_attention_layernorm
- model.layers.9.post_attention_layernorm
- model.layers.10.post_attention_layernorm
- model.layers.11.post_attention_layernorm
- model.layers.12.post_attention_layernorm
- model.layers.13.post_attention_layernorm
- model.layers.14.post_attention_layernorm
- model.layers.15.post_attention_layernorm
- model.layers.16.post_attention_layernorm
- model.layers.17.post_attention_layernorm
- model.layers.18.post_attention_layernorm
- model.layers.19.post_attention_layernorm
- model.layers.20.post_attention_layernorm
- model.layers.21.post_attention_layernorm
- model.layers.22.post_attention_layernorm
- model.layers.23.post_attention_layernorm
- model.layers.24.post_attention_layernorm
- model.layers.25.post_attention_layernorm
- model.layers.26.post_attention_layernorm
- model.layers.27.post_attention_layernorm
- model.layers.28.post_attention_layernorm
- model.layers.29.post_attention_layernorm
- model.layers.30.post_attention_layernorm
- model.layers.31.post_attention_layernorm
# self_attn.k_proj layers
- model.layers.63.self_attn.k_proj
- model.layers.55.self_attn.k_proj
- model.layers.60.self_attn.k_proj
- model.layers.7.self_attn.k_proj
- model.layers.12.self_attn.k_proj
- model.layers.13.self_attn.k_proj
- model.layers.57.self_attn.k_proj
- model.layers.29.self_attn.k_proj
- model.layers.14.self_attn.k_proj
- model.layers.51.self_attn.k_proj
- model.layers.53.self_attn.k_proj
- model.layers.54.self_attn.k_proj
- model.layers.22.self_attn.k_proj
- model.layers.61.self_attn.k_proj
- model.layers.18.self_attn.k_proj
- model.layers.30.self_attn.k_proj
- model.layers.9.self_attn.k_proj
- model.layers.24.self_attn.k_proj
- model.layers.23.self_attn.k_proj
- model.layers.25.self_attn.k_proj
- model.layers.10.self_attn.k_proj
- model.layers.58.self_attn.k_proj
- model.layers.56.self_attn.k_proj
- model.layers.15.self_attn.k_proj
- model.layers.32.self_attn.k_proj
- model.layers.28.self_attn.k_proj
- model.layers.8.self_attn.k_proj
- model.layers.59.self_attn.k_proj
- model.layers.11.self_attn.k_proj
- model.layers.48.self_attn.k_proj
- model.layers.16.self_attn.k_proj
- model.layers.50.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.15.self_attn.o_proj
- model.layers.23.self_attn.o_proj
- model.layers.31.self_attn.o_proj
- model.layers.30.self_attn.o_proj
- model.layers.18.self_attn.o_proj
- model.layers.24.self_attn.o_proj
- model.layers.17.self_attn.o_proj
- model.layers.28.self_attn.o_proj
- model.layers.34.self_attn.o_proj
- model.layers.33.self_attn.o_proj
- model.layers.25.self_attn.o_proj
- model.layers.12.self_attn.o_proj
- model.layers.14.self_attn.o_proj
- model.layers.29.self_attn.o_proj
- model.layers.16.self_attn.o_proj
- model.layers.26.self_attn.o_proj
- model.layers.22.self_attn.o_proj
- model.layers.27.self_attn.o_proj
- model.layers.35.self_attn.o_proj
- model.layers.20.self_attn.o_proj
- model.layers.13.self_attn.o_proj
- model.layers.36.self_attn.o_proj
- model.layers.19.self_attn.o_proj
- model.layers.37.self_attn.o_proj
- model.layers.21.self_attn.o_proj
- model.layers.11.self_attn.o_proj
- model.layers.54.self_attn.o_proj
- model.layers.5.self_attn.o_proj
- model.layers.38.self_attn.o_proj
- model.layers.6.self_attn.o_proj
- model.layers.8.self_attn.o_proj
- model.layers.9.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.1.self_attn.q_proj
- model.layers.2.self_attn.q_proj
- model.layers.3.self_attn.q_proj
- model.layers.45.self_attn.q_proj
- model.layers.54.self_attn.q_proj
- model.layers.35.self_attn.q_proj
- model.layers.48.self_attn.q_proj
- model.layers.61.self_attn.q_proj
- model.layers.52.self_attn.q_proj
- model.layers.50.self_attn.q_proj
- model.layers.60.self_attn.q_proj
- model.layers.56.self_attn.q_proj
- model.layers.58.self_attn.q_proj
- model.layers.42.self_attn.q_proj
- model.layers.59.self_attn.q_proj
- model.layers.44.self_attn.q_proj
- model.layers.55.self_attn.q_proj
- model.layers.57.self_attn.q_proj
- model.layers.41.self_attn.q_proj
- model.layers.36.self_attn.q_proj
- model.layers.39.self_attn.q_proj
- model.layers.4.self_attn.q_proj
- model.layers.43.self_attn.q_proj
- model.layers.34.self_attn.q_proj
- model.layers.46.self_attn.q_proj
- model.layers.49.self_attn.q_proj
- model.layers.40.self_attn.q_proj
- model.layers.25.self_attn.q_proj
- model.layers.51.self_attn.q_proj
- model.layers.17.self_attn.q_proj
- model.layers.37.self_attn.q_proj
- model.layers.53.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.55.self_attn.v_proj
- model.layers.31.self_attn.v_proj
- model.layers.47.self_attn.v_proj
- model.layers.45.self_attn.v_proj
- model.layers.49.self_attn.v_proj
- model.layers.48.self_attn.v_proj
- model.layers.15.self_attn.v_proj
- model.layers.30.self_attn.v_proj
- model.layers.7.self_attn.v_proj
- model.layers.44.self_attn.v_proj
- model.layers.29.self_attn.v_proj
- model.layers.51.self_attn.v_proj
- model.layers.50.self_attn.v_proj
- model.layers.14.self_attn.v_proj
- model.layers.54.self_attn.v_proj
- model.layers.32.self_attn.v_proj
- model.layers.43.self_attn.v_proj
- model.layers.10.self_attn.v_proj
- model.layers.46.self_attn.v_proj
- model.layers.38.self_attn.v_proj
- model.layers.57.self_attn.v_proj
- model.layers.22.self_attn.v_proj
- model.layers.39.self_attn.v_proj
- model.layers.6.self_attn.v_proj
- model.layers.23.self_attn.v_proj
- model.layers.58.self_attn.v_proj
- model.layers.53.self_attn.v_proj
- model.layers.40.self_attn.v_proj
- model.layers.24.self_attn.v_proj
- model.layers.9.self_attn.v_proj
- model.layers.25.self_attn.v_proj
- model.layers.5.self_attn.v_proj


wandb_project: EVA-Qwen2.5-32B-SFFT-v0.0
wandb_entity:
wandb_watch:
wandb_name: Unit-00
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00003
max_grad_norm: 3

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: "unsloth"
# gradient_checkpointing_kwargs:
#   use_reentrant: true
early_stopping_patience:
resume_from_checkpoint: 
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 2
save_safetensors: true
hub_model_id: 
hub_strategy: 
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: true
#   fsdp_offload_params: false  # Changed from true
#   fsdp_use_orig_params: true  # Changed from false
#   fsdp_cpu_ram_efficient_loading: true
#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
#   fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
#   fsdp_activation_checkpointing: true
#   fsdp_state_dict_type: SHARDED_STATE_DICT  # Changed from FULL_STATE_DICT
#   fsdp_sharding_strategy: FULL_SHARD
#   fsdp_forward_prefetch: true  # Added
#   fsdp_backward_prefetch: "BACKWARD_POST"  # Added
#   fsdp_backward_prefetch_limit: 1  # Added
#   fsdp_mixed_precision: BF16  # Added
```

</details><br>