Update README.md

README.md

@@ -18,203 +18,6 @@ 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>
 
-# WHI (Workplace Hazard Identification)
-
-This model is a fine-tuned version of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on a workplace hazard identification dataset. It's designed to analyze incident descriptions and provide structured hazard classifications.
-
-## Model Description
-
-**Model Type:** Mistral-7B (Causal Language Model)
-**Language(s):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-
-## Intended Uses & Limitations
-
-This model is intended for:
-- Analyzing workplace incident descriptions
-- Providing structured hazard classifications
-- Identifying hazard sources and types
-- Generating keywords for database querying related to incidents
-
-It should not be used as a substitute for professional safety assessments or medical advice.
-
-## Training and Evaluation Data
-
-The model was fine-tuned on a custom dataset (`incident_descriptions.json`) containing workplace safety reports. Each entry in the dataset includes:
-- An instruction
-- An incident description
-- A structured output with hazard classification
-
-## Training Procedure
-
-The model was fine-tuned using the Axolotl framework with the following configuration:
-
-```json
-{
-  "_name_or_path": "mistralai/Mistral-7B-v0.1",
-  "architectures": ["MistralForCausalLM"],
-  "attention_dropout": 0.0,
-  "hidden_size": 4096,
-  "num_attention_heads": 32,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 8,
-  "quantization_config": {
-    "load_in_8bit": true,
-    "quant_method": "bitsandbytes"
-  },
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.42.4",
-  "use_cache": false
-}
-```
-
-## How to Use
-
-Here's how you can use this model for workplace hazard identification:
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-# Load model and tokenizer
-model_name = "NimaZahedinameghi/WHI"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-
-# Prepare the input
-instruction = "Given an incident description from a workplace safety report, analyze the text and provide a structured hazard classification. Your response should include the hazard source (broken down into three levels of granularity), the general hazard type, and keywords for database querying related to the incident. Ensure your classification is specific and accurately reflects the details provided in the incident description."
-incident_description = "During the night shift, a worker was operating a forklift in the warehouse. While maneuvering between storage racks, the forklift's rear wheel caught on a piece of loose pallet wrap on the floor. This caused the forklift to swerve suddenly, colliding with a nearby rack. The impact dislodged several heavy boxes from the upper levels, which fell and narrowly missed the worker. The worker managed to stop the forklift and exit safely, but was visibly shaken by the near-miss incident."
-
-# Combine instruction and input
-input_text = f"{instruction}\n\nIncidentDescription: {incident_description}"
-
-# Tokenize and generate
-input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
-output = model.generate(input_ids, max_length=500, num_return_sequences=1, do_sample=True, temperature=0.7)
-
-# Decode and print the result
-result = tokenizer.decode(output[0], skip_special_tokens=True)
-print(result)
-```
-
-This code will generate a structured hazard classification based on the given incident description.
-
-## Limitations and Biases
-
-- The model's performance is limited by the quality and diversity of the training data.
-- It may not accurately classify hazards outside its training domain.
-- The model should not be used as the sole basis for safety decisions; always consult with safety professionals.
-
-## Ethical Considerations
-
-When using this model, consider:
-- Privacy: Ensure that incident descriptions do not contain personally identifiable information.
-- Accountability: The model's outputs should be reviewed by qualified safety professionals.
-- Bias: Be aware of potential biases in the training data that could affect the model's classifications.
-
-## Citation
-
-If you use this model in your research, please cite:
-
-```
-@misc{WHI2023,
-  author = {Nima Zahedinameghi},
-  title = {WHI: Workplace Hazard Identification Model},
-  year = {2023},
-  publisher = {HuggingFace},
-  journal = {HuggingFace Hub},
-  howpublished = {\url{https://huggingface.co/NimaZahedinameghi/WHI}},
-}
-```
-
-axolotl version: `0.4.1`
-```yaml
-base_model: mistralai/Mistral-7B-v0.1
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: NimaZahedinameghi/Workplace-Hazard-Identification
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-hub_model_id: NimaZahedinameghi/WHI
-
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-sample_packing: False
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-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
-
-wandb_project: WHI
-wandb_entity: uqam
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 2
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-save_safetensors: true
-
-```
 
 </details><br>