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Model: Ramikan-BR/tinyllama-coder-py-4bit-v10
Source: Original Platform

.gitattributes

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+tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+tinyllama-coder-py-4bit-v10-unsloth.F16.gguf filter=lfs diff=lfs merge=lfs -text
+tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- sft
+- code
+- lora
+- peft
+base_model: unsloth/tinyllama-chat-bnb-4bit
+pipeline_tag: text-generation
+datasets: Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl
+---
+
+# Uploaded  model
+
+- **Developed by:** Ramikan-BR
+- **Model type:** [text-generation/Python Coder]
+- **Language(s) (NLP):** [en]
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+### Training Data
+
+datasets: [Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl](https://huggingface.co/datasets/Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl)
+
+### Training Procedure
+
+The model was refined using [Unsloath](https://github.com/unslothai/unsloth). The dataset [ise-uiuc/Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K/blob/main/data-oss_instruct-decontaminated.jsonl) was adjusted, leaving only data on python and divided into 10 parts, each refinement occurred for 2 epochs, using adafactor optimizer or adamw_8bit (adafactor seems to deliver less loss).
+
+### Model Sources [optional]
+base_model: [unsloth/tinyllama-chat-bnb-4bit](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit)
+
+model: [Ramikan-BR/tinyllama-coder-py-4bit-v10](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10)
+gguf_f16: [tinyllama-coder-py-4bit-v10-unsloth.F16.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.F16.gguf)
+gguf_Q4_K_M: [tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf)
+gguf_Q8_0: [tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf)
+
+#### Training Hyperparameters
+
+Notebook [Unsloath](https://github.com/unslothai/unsloth) that I used for AI refinement: [TinyLlama](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) 
+```python
+
+%%capture
+# Installs Unsloth, Xformers (Flash Attention) and all other packages!
+!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+!pip install --no-deps xformers trl peft accelerate bitsandbytes # xformers "xformers<0.0.26"
+
+import os
+from google.colab import drive
+drive.mount('/content/drive')
+
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
+fourbit_models = [
+    "unsloth/mistral-7b-bnb-4bit",
+    "unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
+    "unsloth/llama-2-7b-bnb-4bit",
+    "unsloth/llama-2-13b-bnb-4bit",
+    "unsloth/codellama-34b-bnb-4bit",
+    "unsloth/tinyllama-bnb-4bit",
+    "unsloth/gemma-7b-bnb-4bit", # New Google 6 trillion tokens model 2.5x faster!
+    "unsloth/gemma-2b-bnb-4bit",
+] # More models at https://huggingface.co/unsloth
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Ramikan-BR/tinyllama-coder-py-4bit_LORA-v9", # "unsloth/tinyllama" for 16bit loading
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 256, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 512,
+    lora_dropout = 0, # Currently only supports dropout = 0
+    bias = "none",    # Currently only supports bias = "none"
+    use_gradient_checkpointing = True, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+alpaca_prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Input:
+{}
+
+### Output:
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token
+def formatting_prompts_func(examples):
+    inputs       = examples["problem"]
+    outputs      = examples["solution"]
+    texts = []
+    for input, output in zip(inputs, outputs):
+        # Must add EOS_TOKEN, otherwise your generation will go on forever!
+        text = alpaca_prompt.format(input, output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts}
+pass
+
+from datasets import load_dataset
+dataset = load_dataset('json', data_files='/content/drive/MyDrive/data-oss_instruct-py-10.jsonl', split='train')
+dataset = dataset.map(formatting_prompts_func, batched=True)
+
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from unsloth import is_bfloat16_supported
+from transformers.utils import logging
+logging.set_verbosity_info()
+
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    dataset_text_field = "text",
+    max_seq_length = max_seq_length,
+    dataset_num_proc = 2,
+    packing = True, # Packs short sequences together to save time!
+    args = TrainingArguments(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 256,
+        warmup_ratio = 0.1,
+        num_train_epochs = 2,
+        learning_rate = 2e-4,
+        fp16 = not torch.cuda.is_bf16_supported(),
+        bf16 = torch.cuda.is_bf16_supported(),
+        logging_steps = 1,
+        optim = "adafactor", # adamw_torch ou adamw_torch_fused +10% velocidade ou adafactor ou adamw_8bit
+        weight_decay = 0.1,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = "outputs",
+    ),
+)
+
+trainer_stats = trainer.train()
+
+model.save_pretrained("lora_model") # Local saving
+tokenizer.save_pretrained("lora_model")
+model.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving
+tokenizer.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving
+
+# Merge to 16bit
+model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
+model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_16bit", token = "hf_...")
+
+# Merge to 4bit
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",)
+if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_4bit", token = "hf_...")
+
+# Just LoRA adapters
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",)
+if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "lora", token = "hf_...")
+
+# Save to 8bit Q8_0
+model.save_pretrained_gguf("model", tokenizer,)
+model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, token = "hf_...")
+
+# Save to 16bit GGUF
+model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16")
+model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "f16", token = "hf_...")
+
+# Save to q4_k_m GGUF
+model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
+model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "q4_k_m", token = "hf_...")
+
+Loss for 5 epochs in the last training session of the last part of the dataset:
+==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
+   \\   /|    Num examples = 407 | Num Epochs = 5
+O^O/ \_/ \    Batch size per device = 2 | Gradient Accumulation steps = 256
+\        /    Total batch size = 512 | Total steps = 5
+ "-____-"     Number of trainable parameters = 201,850,880
+ [5/5 29:36, Epoch 3/5]
+Step	Training Loss
+1	0.568000
+2	0.145300
+3	0.506100
+4	0.331900
+5	0.276100
+
+Quick test 1 after training the last part of the dataset:
+
+# alpaca_prompt = Copied from above
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "Continue the fibonnaci sequence.", # instruction
+        "1, 1, 2, 3, 5, 8", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+AI Response: ['<s> Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Input:\nContinue the fibonnaci sequence.\n\n### Output:\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 420, 787, 1444, 2881, 4765, 8640']
+
+Quick test 2 after training the last part of the dataset:
+
+# alpaca_prompt = Copied from above
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "Continue the fibonnaci sequence.", # instruction
+        "1, 1, 2, 3, 5, 8", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
+
+AI Response: <s> Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Input:
+Continue the fibonnaci sequence.
+
+### Output:
+1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 420, 787, 1444, 2881, 4765, 8640, 17281, 31362, 65325, 128672, 251345, 410000, 720000, 1280000,
+
+Quick test 3 after training the last part of the dataset:
+
+if False:
+    from unsloth import FastLanguageModel
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
+        max_seq_length = max_seq_length,
+        dtype = dtype,
+        load_in_4bit = load_in_4bit,
+    )
+    FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# alpaca_prompt = You MUST copy from above!
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "What is a famous tall tower in Paris?", # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)
+
+AI Response: <s> Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Input:
+What is a famous tall tower in Paris?
+
+### Output:
+The famous tall tower in Paris is the Eiffel Tower. It is a 300-meter-tall steel tower located in the heart of Paris, France. The tower was built in 18892 and is a popular tourist attraction. It is also a symbol of the city
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+```
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

config.json

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+{
+  "_name_or_path": "unsloth/tinyllama-chat-bnb-4bit",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 4096,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 22,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "linear"
+  },
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.40.2",
+  "unsloth_version": "2024.5",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "max_length": 2048,
+  "pad_token_id": 0,
+  "transformers_version": "4.40.2"
+}

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+size 2200119664

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tinyllama-coder-py-4bit-v10-unsloth.F16.gguf

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tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 4096,
+  "pad_token": "<unk>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}