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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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- llama-cpp |
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- gguf-my-repo |
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license: apache-2.0 |
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language: |
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- en |
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base_model: EVA-UNIT-01/EVA-Qwen2.5-1.5B-v0.0 |
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datasets: |
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- anthracite-org/kalo-opus-instruct-22k-no-refusal |
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- Nopm/Opus_WritingStruct |
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned |
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- Gryphe/Sonnet3.5-Charcard-Roleplay |
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- Gryphe/ChatGPT-4o-Writing-Prompts |
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned |
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned |
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- nothingiisreal/Reddit-Dirty-And-WritingPrompts |
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- allura-org/Celeste-1.x-data-mixture |
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- cognitivecomputations/dolphin-2.9.3 |
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model-index: |
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- name: EVA-Qwen2.5-1.5B-FFT-v0.0 |
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results: [] |
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--- |
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# Triangle104/EVA-Qwen2.5-1.5B-v0.0-Q4_K_S-GGUF |
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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. |
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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. |
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--- |
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Model details: |
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- |
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A small-scale RP/storywriting specialist model, full-parameter |
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finetune of Qwen2.5-1.5B on mixture of synthetic and natural data. |
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It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve |
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versatility, creativity and "flavor" of the resulting model. |
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Unlike EVA-D 1.5B v0.0, this model was created without using |
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DistillKit, and unlike other versions of EVA, Spectrum wasn't used |
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either, since layer freezing is inefficient at small scale. |
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Training data: |
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Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's card for details. |
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Kalomaze's Opus_Instruct_25k dataset, filtered for refusals. |
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A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe |
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A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe |
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Synthstruct and SynthRP datasets by Epiculous |
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A subset from Dolphin-2.9.3, including filtered version of not_samantha and a small subset of systemchat. |
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Training time and hardware: |
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9 hours on 4x3090Ti |
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Model was created by Kearm, Auri and Cahvay. |
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Special thanks: |
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to Cahvay for his work on investigating and reprocessing the |
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corrupted dataset, removing the single biggest source of data poisoning. |
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to Gryphe, Lemmy, Kalomaze, Nopm, Epiculous and CognitiveComputations for the data |
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and to Allura-org for support, feedback, beta-testing and doing quality control of EVA models. |
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See axolotl config |
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axolotl version: 0.4.1 |
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base_model: /media/kearm/Disk_2/HF_FAST_MoE_Fodder/Qwen2.5-1.5B |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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plugins: |
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- axolotl.integrations.liger.LigerPlugin |
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liger_rope: true |
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liger_rms_norm: true |
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liger_swiglu: true |
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liger_fused_linear_cross_entropy: true |
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# plugins: |
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# - axolotl.integrations.spectrum.SpectrumPlugin |
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# spectrum_top_fraction: 0.5 |
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# # Optional if using a pre-scanned model as your base_model. Useful if using a model mirror |
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# spectrum_model_name: Qwen/Qwen2.5-32B |
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datasets: |
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- path: datasets/Celeste_Filtered_utf8fix.jsonl |
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type: sharegpt |
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- path: datasets/deduped_not_samantha_norefusals.jsonl |
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type: sharegpt |
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- path: datasets/deduped_SynthRP-Gens_processed_ShareGPT_converted_cleaned.jsonl |
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type: sharegpt |
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- path: datasets/deduped_Synthstruct-Gens_processed_sharegpt_converted_cleaned.jsonl |
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type: sharegpt |
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- path: datasets/Gryphe-4o-WP-filtered-sharegpt_utf8fix.jsonl |
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type: sharegpt |
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- path: datasets/Sonnet3-5-charcard-names-filtered-sharegpt_utf8fix.jsonl |
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type: sharegpt |
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- path: datasets/SystemChat_subset_filtered_sharegpt_utf8fix.jsonl |
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type: sharegpt |
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- path: datasets/S2.jsonl |
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type: sharegpt |
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- path: datasets/Turing.jsonl |
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type: sharegpt |
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chat_template: chatml |
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shuffle_merged_datasets: true |
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val_set_size: 0.05 |
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output_dir: EVA-Qwen2.5-1.5B-FFT-v0.0 |
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sequence_len: 10240 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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# adapter: qlora |
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# lora_model_dir: |
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# lora_r: 64 |
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# lora_alpha: 128 |
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# lora_dropout: 0.05 |
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# lora_target_linear: true |
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# peft_use_dora: true |
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wandb_project: EVA-Qwen2.5-1.5B-FFT-v0.0 |
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wandb_entity: |
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wandb_watch: |
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wandb_name: Unit-00 |
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wandb_log_model: |
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gradient_accumulation_steps: 8 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.000005 |
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max_grad_norm: 1.5 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: "unsloth" |
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gradient_checkpointing_kwargs: |
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use_reentrant: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 20 |
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evals_per_epoch: 4 |
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saves_per_epoch: 4 |
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save_safetensors: true |
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save_total_limit: 8 |
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hub_model_id: |
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hub_strategy: |
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debug: |
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deepspeed: deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.15 |
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# fsdp: |
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# - full_shard |
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# - auto_wrap |
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# fsdp_config: |
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# fsdp_limit_all_gathers: true |
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# fsdp_sync_module_states: false |
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# fsdp_offload_params: true |
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# fsdp_cpu_ram_efficient_loading: true |
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# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
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# fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer |
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# fsdp_activation_checkpointing: true |
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# fsdp_state_dict_type: SHARDED_STATE_DICT # Changed from FULL_STATE_DICT |
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# fsdp_sharding_strategy: FULL_SHARD |
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# fsdp_forward_prefetch: false # Added |
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# fsdp_backward_prefetch: "BACKWARD_PRE" # Added |
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# fsdp_backward_prefetch_limit: 1 # Added |
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# fsdp_mixed_precision: BF16 # Added |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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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" |
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``` |
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### Server: |
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```bash |
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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 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./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" |
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``` |
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or |
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``` |
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./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 |
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``` |
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