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-Q4_K_S-GGUF
This model was converted to GGUF format from EVA-UNIT-01/EVA-Qwen2.5-1.5B-v0.0 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card 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)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q4_K_S-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q4_K_S-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q4_k_s.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps 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-Q4_K_S-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q4_K_S-GGUF --hf-file eva-qwen2.5-1.5b-v0.0-q4_k_s.gguf -c 2048