README.md

---
license: apache-2.0
language:
- en
base_model: FourOhFour/Tulu-3.69-DPO-8B
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Tulu-3.69-DPO-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`FourOhFour/Tulu-3.69-DPO-8B`](https://huggingface.co/FourOhFour/Tulu-3.69-DPO-8B) 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/FourOhFour/Tulu-3.69-DPO-8B) for more details on the model.

---
Model details:
-
This is a DPO applied over Tulu-3.69-8B. This model is designed to 
roleplay and converse like a human chat partner. This model follows 
instructions well and excels at playing characters in a realistic and 
entertaining manner.


For ease of use, try the Llama 3 instruct format. You may need to set a custom stop string for <|end_of_text|>


For optimal performance I have found that a modified Tulu 3 instruct format is quite effective:


<|system|>


This is an instruction.


<|end_of_text|>


<|user|>


This is the user input.


<|assistant|>


This is model output.


<|end_of_text|>


Further, if you want your bot to have a sense of time, you can set the last output prefix as such:


<|system|>


{{time}} {{weekday}} {{date}}


<|end_of_text|>


<|assistant|>


Note: these macros may differ in your chosen inferencing frontend. Please correct accordingly.


base_model: jeiku/Tulu-3.69-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

hub_model_id: jeiku/tuludpo
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

chat_template: llama3
rl: dpo
datasets:
  - path: antiven0m/physical-reasoning-dpo
    type: llama3.prompt_pairs
  - path: nbeerbower/Purpura-DPO
    type: llama3.prompt_pairs
  - path: FourOhFour/Human_DPO_Emojis_Removed
    type: llama3.prompt_pairs

shuffle_merged_datasets: true
val_set_size: 0.005
output_dir: ./outputs/out

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

wandb_project: evil
wandb_entity:
wandb_watch:
wandb_name: evil
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
weight_decay: 0.05

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: 
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 
saves_per_epoch: 1

debug:
deepspeed:
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>

---
## 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/Tulu-3.69-DPO-8B-Q4_K_M-GGUF --hf-file tulu-3.69-dpo-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Tulu-3.69-DPO-8B-Q4_K_M-GGUF --hf-file tulu-3.69-dpo-8b-q4_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/Tulu-3.69-DPO-8B-Q4_K_M-GGUF --hf-file tulu-3.69-dpo-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Tulu-3.69-DPO-8B-Q4_K_M-GGUF --hf-file tulu-3.69-dpo-8b-q4_k_m.gguf -c 2048
```