Update README.md

README.md

@@ -1,202 +1,127 @@
 ---
-base_model: nltpt/Llama-3.2-1B-Instruct
+base_model:
+- unsloth/Llama-3.2-1B-Instruct
 library_name: peft
+language:
+- en
+license: cc0-1.0
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-### Framework versions
-
-- PEFT 0.13.1
+# A !!!!!disclaimer uh. for now, the experimentation does not lead me anywhere due to limit resources that I have and do not recommend to download this model. Working on working on it.
+
+PEFT Finnegan-tuned LLaMA 3.2-1B-instruct on part of Finnegans Wake dataset for text generation in the style of James Joyce.
+
+## Iteration 1:
+
+Dataset I prepared like that:
+```
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Load the text
+with open(INPUT_FILE, "r", encoding="utf-8") as file:
+    text = file.read()
+
+# Tokenize the text
+tokens = tokenizer.encode(text, truncation=False, add_special_tokens=False)
+
+# Split tokens into chunks
+chunks = [tokens[i:i + CHUNK_SIZE] for i in range(0, len(tokens), CHUNK_SIZE)]
+
+# Prepare dataset
+dataset = []
+for chunk in chunks:
+    chunk_text = tokenizer.decode(chunk, skip_special_tokens=True)
+    
+    # Split the chunk into three parts randomly
+    split_points = sorted(random.sample(range(len(chunk_text)), 2))  # Two random split points
+    context = chunk_text[:split_points[0]]
+    instruction = chunk_text[split_points[0]:split_points[1]]
+    response = chunk_text[split_points[1]:]
+    
+    # Add to dataset
+    dataset.append({
+        "context": context,
+        "instruction": instruction,
+        "response": response,
+    })
+
+# Save dataset locally as a .jsonl file
+with open(OUTPUT_FILE, "w", encoding="utf-8") as file:
+    for item in dataset:
+        json.dump(item, file)
+        file.write("\n")
+
+print(f"Dataset saved locally to {OUTPUT_FILE}")
+```
+
+Example of dataset entry:
+```
+{"context": "riverrun, past Eve and Adam's, from swerve of shore to bend of bay...", "instruction": "Sir Tristram, violer d'amores, fr'over the short sea...", "response": "O here here how hoth sprowled met the duskt the father of fornicationists..."}
+```
+
+fine-tuned on 1/10th of text on fireworks.ai with params:
+```
+dataset: finnegans_wake_dataset
+
+text_completion:
+  # How the fields of the JSON dataset should be formatted into the input text
+  input_template: "### GIVEN THE CONTEXT: {context}  ### INSTRUCTION: {instruction}  ### RESPONSE IS: "
+
+  # How the fields of the JSON dataset should be formatted into the output text
+  output_template: "ANSWER: {response}"
+
+# The Fireworks model name of the base model
+base_model: accounts/fireworks/models/llama-v3p2-1b
+
+# Hyperparameters for fine-tuning (should be passed as args and removed from here)
+hyperparameters:
+  learning_rate: 1e-5  # Learning rate for the optimizer
+  epochs: 1            # Number of epochs to train
+  batch_size: 4        # Batch size for training
+```
+
+Spent 5 mins on tuning and $0.01 from my free credits. 
+
+Result: Seemingly not enough data to affect model output.
+
+## Iteration 2:
+
+All same (forgot to save config with new dataset).
+
+finnetune.yaml:
+```
+# The ID of the dataset you created
+dataset: huivam-finnegans-2
+
+# Configuration for text completion fine-tuning
+text_completion:
+  # How the fields of the JSON dataset should be formatted into the input text
+  input_template: "### GIVEN THE CONTEXT: {context}  ### INSTRUCTION: {instruction}  ### RESPONSE IS: "
+
+  # How the fields of the JSON dataset should be formatted into the output text
+  output_template: "ANSWER: {response}"
+
+# The Fireworks model name of the base model
+base_model: accounts/fireworks/models/llama-v3p2-1b-instruct
+```
+
+Finne-tuning commands used:
+```
+./firectl create dataset huivam-finnegans-2 .\finnegans_wake_dataset_2.jsonl
+./firectl create fine-tuning-job --settings-file finnetune.yaml --epochs=3 --learning-rate=2e-5 --batch-size=8
+```
+
+New params used to finne-tune:
+```
+Text Completion:
+  Input Template: ### GIVEN THE CONTEXT: {context}  ### INSTRUCTION: {instruction}  ### RESPONSE IS:
+  Output Template: ANSWER: {response}
+Base Model: accounts/fireworks/models/llama-v3p2-1b-instruct
+Epochs: 3
+Learning Rate: 2e-05
+Lora Rank: 8
+Batch Size: 8
+Evaluation Split: 0
+```
+
+Spent: $0.08