LemiSt
/

SmolLM-135M-instruct-de-merged

@@ -1,199 +1,165 @@
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 ---
+base_model: LemiSt/SmolLM-135M-de
+library_name: peft
+license: apache-2.0
+tags:
+- axolotl
+- generated_from_trainer
+model-index:
+- name: SmolLM-135M-instruct-de
+  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+axolotl version: `0.4.1`
+```yaml
+base_model: LemiSt/SmolLM-135M-de
+model_type: LlamaForCausalLM
+tokenizer_type: GPT2Tokenizer
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: smollm_dataset.json
+    type: sharegpt
+    conversation: chatml
+chat_template: chatml
+default_system_prompt: "Du bist ein hilfreicher KI-Assistent."
+dataset_prepared_path:
+val_set_size: 0.05
+adapter: qlora
+lora_model_dir:
+sequence_len: 2048
+sample_packing: true
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+wandb_project: smollm-135m-de-sft-qlora
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+output_dir: ./outputs/smollm-135m-sft-qlora-out
+hub_model_id: LemiSt/SmolLM-135M-instruct-de
+hub_strategy: end
+gradient_accumulation_steps: 16
+micro_batch_size: 2
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+torchdistx_path:
+lr_scheduler: cosine
+learning_rate: 0.003
+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
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 20
+evals_per_epoch: 4
+saves_per_epoch: 4
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<|endoftext|>"
+  eos_token: "<|endoftext|>"
+  unk_token: "<|endoftext|>"
+```
+</details><br>
+# SmolLM-135M-instruct-de
+MERGED VERSION: [LemiSt/SmolLM-135M-instruct-de-merged](https://huggingface.co/LemiSt/SmolLM-135M-instruct-de-merged)
+This model is a fine-tuned version of [LemiSt/SmolLM-135M-de](https://huggingface.co/LemiSt/SmolLM-135M-de) on an internal testing dataset with general chat examples.
+It achieves the following results on the evaluation set:
+- Loss: 0.7453
+## Model description
+For more information, see the mode card of the [base model](https://huggingface.co/LemiSt/SmolLM-135M-de). This adapter was trained using qlora at rank 32 with alpha 16, applying a dataset of around 200k german chat samples for two epochs.
+## Intended uses & limitations
+Mainly playing around with tiny chat models - while the output is generally intact German and the model somewhat follows instructions, it makes too many mistakes to be deployed in a real world setting.
+### Usage example
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+checkpoint = "LemiSt/SmolLM-135M-instruct-de"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map=device, torch_dtype=torch.bfloat16)
+messages = [
+  {"role": "system", "content": "Du bist ein hilfreicher Assistent."},
+  {"role": "user", "content": "Wie viele Hände hat ein normaler Mensch?"}
+]
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt", add_generation_prompt=True).to(device)
+outputs = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.3, top_p=0.9, repetition_penalty=1.2)
+print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+## Training and evaluation data
+Internal dataset which was compiled for another experiment.
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.003
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 20
+- num_epochs: 2
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.6406        | 0.0005 | 1    | 1.6172          |
+| 0.8219        | 0.2497 | 501  | 0.8901          |
+| 0.8646        | 0.4995 | 1002 | 0.8370          |
+| 0.8651        | 0.7492 | 1503 | 0.8052          |
+| 0.7231        | 0.9989 | 2004 | 0.7827          |
+| 0.7632        | 1.2468 | 2505 | 0.7673          |
+| 0.7543        | 1.4967 | 3006 | 0.7536          |
+| 0.7782        | 1.7466 | 3507 | 0.7469          |
+| 0.6724        | 1.9966 | 4008 | 0.7453          |
+### Framework versions
+- PEFT 0.12.0
+- Transformers 4.45.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1