iko-01
/

LLaMA-1

@@ -1,208 +1,143 @@
 ---
-base_model: Qwen/Qwen2-0.5B
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2-0.5B
-- llama-factory
-- lora
-- transformers
 ---
-# 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 0.17.1

 ---
+license: apache-2.0
+language:
+  - en
 library_name: peft
 tags:
+  - text-generation
+  - transformers
+  - peft
+  - lora
+  - qwen
+  - qwen2
+  - reddit
+  - llama-factory
+datasets:
+  - olmo-data/dolma-v1_6-reddit
+base_model: Qwen/Qwen2-0.5B
+pipeline_tag: text-generation
 ---
+# Qwen2-0.5B Reddit LoRA Adapter
+**Repo:** [iko-01/LLaMA-1](https://huggingface.co/iko-01/LLaMA-1)
+**Base model:** [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B)
+**Adapter type:** LoRA (via LLaMA-Factory + QLoRA)
+**Intended use:** Simulating casual, Reddit-style comments, discussions, and thread replies
+## Model Description
+This is a **LoRA adapter** fine-tuned on top of **Qwen2-0.5B** using a filtered subset of Reddit posts & comments from the Dolma dataset (v1.6 Reddit portion).
+The model is trained to generate informal, conversational text typical of Reddit threads — including sarcasm, memes references, casual opinions, upvotes/downvotes vibe, and natural thread continuations.
+Despite the repository name (`LLaMA-1`), this is **not** a LLaMA model — it is purely **Qwen2** architecture.
+### Key Characteristics
+- Extremely lightweight (only ~0.5B base + small LoRA adapter)
+- Runs comfortably on consumer GPUs, laptops, or even decent CPUs
+- Fast inference (very suitable for local prototyping, chatbots, Reddit simulators, etc.)
+- Casual / internet / meme-friendly tone
 ## Training Details
+- **Framework:** LLaMA-Factory
+- **Training method:** QLoRA (4-bit base quantization + LoRA)
+- **Dataset size:** ~6,000 high-quality, deduplicated Reddit samples
+- **Hardware:** Google Colab T4 (single GPU)
+- **Training duration:** ≈ 30 minutes
+- **Hyperparameters:**
+  | Parameter              | Value     |
+  |------------------------|-----------|
+  | LoRA rank (r)          | 32        |
+  | LoRA alpha             | 64        |
+  | Learning rate          | 2e-4      |
+  | Batch size             | 2         |
+  | Gradient accumulation  | 16        |
+  | Epochs                 | 3         |
+  | Optimizer              | AdamW     |
+  | Warmup ratio           | 0.03      |
+## Usage
+```bash
+pip install -U transformers peft torch accelerate bitsandbytes  # bitsandbytes optional but recommended
+```
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base_model_id = "Qwen/Qwen2-0.5B"
+adapter_id    = "iko-01/LLaMA-1"
+# Load base model
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+# Apply LoRA adapter
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+# Example prompt
+prompt = """Continue this r/AskReddit thread:
+After playing for 50 hours I finally"""
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    out = model.generate(
+        **inputs,
+        max_new_tokens=120,
+        temperature=0.75,
+        top_p=0.92,
+        repetition_penalty=1.08,
+        do_sample=True
+    )
+response = tokenizer.decode(out[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+```
+### Example Outputs
+**Prompt:**
+`Complete this r/gaming discussion: After playing for 50 hours I finally`
+**Typical model output:**
+`...realized the main story is mid but the side content is actually peak. The open world exploration in the frozen north hits different. Spent like 6 hours just fishing and upgrading my house and I don't even feel bad about it lmao. Anyone else 100% the fishing minigame before the final boss?`
+## Limitations & Responsible Use
+- **Model size** — Being a 0.5B model, it has limited world knowledge, reasoning depth, and coherence over very long contexts compared to 7B+ models.
+- **Reddit bias** — The training data comes from Reddit → expect informal language, slang, sarcasm, exaggeration, memes, controversial/hot-take opinions, and sometimes toxic phrasing.
+- **Hallucinations** — Can confidently generate plausible but incorrect facts, especially outside popular Reddit topics.
+- **Not for production / sensitive use** — Not suitable for factual Q&A, customer support, education, legal/medical advice, or any high-stakes application.
+- **English only** — The fine-tune was done exclusively on English Reddit content.
+Use this model mainly for **creative**, **entertainment**, or **research** purposes (e.g. generating synthetic discussion data, building Reddit-style bots, style transfer experiments).
+## Citation / Thanks
+If you use this adapter in your work, feel free to mention:
+> Fine-tuned with LLaMA-Factory on Qwen2-0.5B using Reddit data from Dolma.
+Big thanks to the Qwen team, LLaMA-Factory contributors, and AllenAI (Dolma dataset).
+Happy hacking! 🚀
+```