Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceH4/no_robots
+language:
+- en
+pipeline_tag: text-generation
+thumbnail: https://huggingface.co/mrm8488/limstral-7B-v0.1/resolve/main/limstral_logo.png
+---
+
+## Mistral 7B fine-tuned on H4/No Robots instructions
+This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) dataset for instruction following downstream task.
+
+## Training procedure
+
+The model was loaded on **8 bits** and fine-tuned on the LIMA dataset using the **LoRA** PEFT technique with the `huggingface/peft` library and `trl/sft` for one epoch on 1 x A100 (40GB) GPU.
+
+SFT Trainer params:
+```
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=train_ds,
+    eval_dataset=test_ds,
+    peft_config=peft_config,
+    dataset_text_field="text",
+    max_seq_length=2048,
+    tokenizer=tokenizer,
+    args=training_arguments,
+    packing=False
+)
+```
+
+LoRA config:
+```
+config = LoraConfig(
+        lora_alpha=16,
+        lora_dropout=0.1,
+        r=64,
+        bias="none",
+        task_type="CAUSAL_LM",
+        target_modules = ['q_proj', 'k_proj', 'down_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj']
+    )
+```
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 66
+- gradient_accumulation_steps: 64
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Step | Training Loss | Validation Loss |
+|------|---------------|-----------------|
+| 10   | 1.796200      | 1.774305        |
+| 20   | 1.769700      | 1.679720        |
+| 30   | 1.626800      | 1.667754        |
+| 40   | 1.663400      | 1.665188        |
+| 50   | 1.565700      | 1.659000        |
+| 60   | 1.660300      | 1.658270        |
+
+
+
+
+### Usage
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+repo_id = "mrm8488/mistral-7b-ft-h4-no_robots_instructions"
+
+model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+gen = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+
+instruction = "[INST] Write an email to say goodbye to me boss [\INST]"
+res = gen(instruction, max_new_tokens=512, temperature=0.3, top_p=0.75, top_k=40, repetition_penalty=1.2, eos_token_id=2)
+print(res[0]['generated_text'])
+```
+
+### Framework versions
+
+- Transformers 4.35.0.dev0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1