Create README.md

README.md

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+---
+license: other
+tags:
+- axolotl
+- generated_from_trainer
+base_model: chargoddard/internlm2-20b-llama
+model-index:
+- name: Stellaris-internlm2-20b-r512
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.3.0`
+```yaml
+base_model: chargoddard/internlm2-20b-llama
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: ARB/arb_law.json
+    ds_type: json
+    type: alpaca
+    conversation: chatml
+
+  - path: ARB/arb_math.json
+    ds_type: json
+    type: alpaca
+    conversation: chatml
+
+  - path: ARB/arb_mcat_reading.json
+    ds_type: json
+    type: alpaca
+    conversation: chatml
+
+  - path: ARB/arb_mcat_science.json
+    ds_type: json
+    type: alpaca
+    conversation: chatml
+
+  - path: ARB/arb_physics.json
+    ds_type: json
+    type: alpaca
+    conversation: chatml
+
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./Weyaxi-test
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+
+lora_r: 512
+lora_alpha: 256
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+
+wandb_project: huggingface 
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+hub_model_id: Weyaxi/Weyaxi-test
+
+gradient_accumulation_steps: 4 # change
+micro_batch_size: 2 # change
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+
+save_steps: 20
+save_total_limit: 5
+
+debug:
+#deepspeed: deepspeed/zero3_bf16.json
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  eos_token: "<|im_end|>"
+tokens:
+  - "<|im_start|>"
+```
+
+</details><br>
+
+# Weyaxi-test
+
+This model is a fine-tuned version of [chargoddard/internlm2-20b-llama](https://huggingface.co/chargoddard/internlm2-20b-llama) on the None dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: True
+- load_in_4bit: False
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: fp4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float32
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- PEFT 0.7.0
+- Transformers 4.37.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.0