README.md

---
library_name: transformers
tags:
- generated_from_trainer
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
base_model: EVA-UNIT-01/EVA-Qwen2.5-1.5B-v0.0
datasets:
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- Nopm/Opus_WritingStruct
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Gryphe/Sonnet3.5-Charcard-Roleplay
- Gryphe/ChatGPT-4o-Writing-Prompts
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- nothingiisreal/Reddit-Dirty-And-WritingPrompts
- allura-org/Celeste-1.x-data-mixture
- cognitivecomputations/dolphin-2.9.3
model-index:
- name: EVA-Qwen2.5-1.5B-FFT-v0.0
  results: []
---

# Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q5_K_M-GGUF
This model was converted to GGUF format from [`EVA-UNIT-01/EVA-Qwen2.5-1.5B-v0.0`](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-1.5B-v0.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-1.5B-v0.0) for more details on the model.

---
Model details:
-

  A small-scale RP/storywriting specialist model, full-parameter 
finetune of Qwen2.5-1.5B on mixture of synthetic and natural data.

  It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve 
versatility, creativity and "flavor" of the resulting model.

  Unlike EVA-D 1.5B v0.0, this model was created without using 
DistillKit, and unlike other versions of EVA, Spectrum wasn't used 
either, since layer freezing is inefficient at small scale.





  

  

    Training data:
  

    
Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's card for details.
Kalomaze's Opus_Instruct_25k dataset, filtered for refusals.
A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe
A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe
Synthstruct and SynthRP datasets by Epiculous
A subset from Dolphin-2.9.3, including filtered version of not_samantha and a small subset of systemchat.

  

     Training time and hardware:
  

      
9 hours on 4x3090Ti

  




  
Model was created by Kearm, Auri and Cahvay.

  
Special thanks:
to Cahvay for his work on investigating and reprocessing the 
corrupted dataset, removing the single biggest source of data poisoning.
to Gryphe, Lemmy, Kalomaze, Nopm, Epiculous and CognitiveComputations for the data
and to Allura-org for support, feedback, beta-testing and doing quality control of EVA models.





See axolotl config


axolotl version: 0.4.1


base_model: /media/kearm/Disk_2/HF_FAST_MoE_Fodder/Qwen2.5-1.5B

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/Celeste_Filtered_utf8fix.jsonl
    type: sharegpt
  - path: datasets/deduped_not_samantha_norefusals.jsonl
    type: sharegpt
  - path: datasets/deduped_SynthRP-Gens_processed_ShareGPT_converted_cleaned.jsonl
    type: sharegpt
  - path: datasets/deduped_Synthstruct-Gens_processed_sharegpt_converted_cleaned.jsonl
    type: sharegpt
  - path: datasets/Gryphe-4o-WP-filtered-sharegpt_utf8fix.jsonl
    type: sharegpt
  - path: datasets/Sonnet3-5-charcard-names-filtered-sharegpt_utf8fix.jsonl
    type: sharegpt
  - path: datasets/SystemChat_subset_filtered_sharegpt_utf8fix.jsonl
    type: sharegpt
  - path: datasets/S2.jsonl
    type: sharegpt
  - path: datasets/Turing.jsonl
    type: sharegpt

chat_template: chatml
shuffle_merged_datasets: true
val_set_size: 0.05
output_dir: EVA-Qwen2.5-1.5B-FFT-v0.0

sequence_len: 10240
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

# adapter: qlora
# lora_model_dir:
# lora_r: 64
# lora_alpha: 128
# lora_dropout: 0.05
# lora_target_linear: true
# peft_use_dora: true

wandb_project: EVA-Qwen2.5-1.5B-FFT-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: 0.000005
max_grad_norm: 1.5

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

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: 4
save_safetensors: true
save_total_limit: 8
hub_model_id:
hub_strategy:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.15
# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: false
#   fsdp_offload_params: true
#   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: false  # Added
#   fsdp_backward_prefetch: "BACKWARD_PRE"  # Added
#   fsdp_backward_prefetch_limit: 1  # Added
#   fsdp_mixed_precision: BF16  # Added

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q5_K_M-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q5_K_M-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q5_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q5_K_M-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q5_K_M-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q5_k_m.gguf -c 2048
```