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README.md

@@ -1,127 +1,202 @@
 ---
-base_model:
-- unsloth/Llama-3.2-1B-Instruct
+base_model: nltpt/Llama-3.2-1B-Instruct
 library_name: peft
-language:
-- en
-license: cc0-1.0
 ---
-# 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
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.1

adapter_config.json

@@ -20,13 +20,13 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "k_proj",
-    "down_proj",
-    "q_proj",
     "up_proj",
-    "gate_proj",
     "o_proj",
-    "v_proj"
+    "v_proj",
+    "k_proj",
+    "gate_proj",
+    "q_proj",
+    "down_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

adapter_model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:0669062b8544ccc74f5f766878f5782a2d5839f4132e03d3b129989817c279a8
+oid sha256:7af5870423f4ddb671e0f24481d6581736ed069347803ed1717023dde02531d6
 size 11301520

fireworks.json

@@ -0,0 +1,10 @@
+{
+    "defaults": {
+        "stop": [
+            "<|end_of_text|>",
+            "<|eom_id|>",
+            "<|eot_id|>"
+        ]
+    },
+    "base_model": "accounts/fireworks/models/llama-v3p1-8b-instruct"
+}