witness_reliability_run1_merged
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset.
Model description
More information needed
Intended uses & limitations
Usage
merged_model_name = "isaaclee/mistral_train_run4_merged"
task_type = 'CAUSAL_LM'
tokenizer = AutoTokenizer.from_pretrained(merged_model_name)
model = AutoModelForCausalLM.from_pretrained(merged_model_name)
pipe = pipeline(task_type, model=model, tokenizer=tokenizer, temperature=0.0)
result = pipe(prompt, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
answer = result[0]['generated_text'][len(prompt):].strip()
Map the answer as
| answer | inference |
|---|---|
| a | average |
| question | questionable |
| re | reliable |
| second | second-hand |
| all else | average |
Since the model is fundamentally a LLM, it might generate texts that are not in the defined set of values ['a', 'question', 're', 'second'].
In those cases, default to average, as indicated by the "all else" in the table above.
Training and evaluation data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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