syaffers
/

tiny-random-llama-lora

@@ -2,206 +2,118 @@
 base_model: hmellor/tiny-random-LlamaForCausalLM
 library_name: peft
 pipeline_tag: text-generation
 tags:
 - base_model:adapter:hmellor/tiny-random-LlamaForCausalLM
 - lora
 - transformers
 ---
-# Model Card for Model ID
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 ### Framework versions
-- PEFT 0.18.0

 base_model: hmellor/tiny-random-LlamaForCausalLM
 library_name: peft
 pipeline_tag: text-generation
+license: mit
+language:
+- en
+datasets:
+- iamholmes/tiny-imdb
 tags:
 - base_model:adapter:hmellor/tiny-random-LlamaForCausalLM
 - lora
 - transformers
+- causal-lm
+- vllm
 ---
+# Tiny Random LLaMA LoRA
+A minimal LoRA adapter for [hmellor/tiny-random-LlamaForCausalLM](https://huggingface.co/hmellor/tiny-random-LlamaForCausalLM), useful for smoke testing deployments.
 ## Model Details
 ### Model Description
+This is a LoRA (Low-Rank Adaptation) adapter trained on a tiny random LLaMA model. The model and adapter are intentionally small and produce random outputs—they are **not** meant for any real inference tasks. The primary purpose is to provide a lightweight adapter for testing deployment pipelines, inference servers, and LoRA loading mechanisms.
+- **Model type:** LoRA adapter for causal language modeling
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from:** [hmellor/tiny-random-LlamaForCausalLM](https://huggingface.co/hmellor/tiny-random-LlamaForCausalLM)
+### Model Sources
+- **Repository:** [syaffers/tiny-random-llama-lora](https://github.com/syaffers/tiny-random-llama-lora) (training code)
 ## Uses
 ### Direct Use
+This adapter is intended for:
+- Smoke testing LoRA adapter loading in inference pipelines
+- Testing deployment configurations with minimal resource usage
+- Validating HuggingFace PEFT integration in your infrastructure
 ### Out-of-Scope Use
+This model should **not** be used for:
+- Any real text generation or NLP tasks
+- Production applications
+- Any use case requiring meaningful outputs
 ## How to Get Started with the Model
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base_model = AutoModelForCausalLM.from_pretrained("hmellor/tiny-random-LlamaForCausalLM")
+model = PeftModel.from_pretrained(base_model, "syaffers/tiny-random-llama-lora")
+tokenizer = AutoTokenizer.from_pretrained("syaffers/tiny-random-llama-lora")
+# Generate (output will be random/meaningless)
+inputs = tokenizer("Hello world", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=10)
+print(tokenizer.decode(outputs[0]))
+```
 ## Training Details
 ### Training Data
+[iamholmes/tiny-imdb](https://huggingface.co/datasets/iamholmes/tiny-imdb) - A tiny subset of IMDB reviews used for quick training iterations.
 ### Training Procedure
 #### Training Hyperparameters
+- **Training regime:** fp32
+- **Batch size:** 4
+- **Learning rate:** 1e-4
+- **Epochs:** 3
+- **Warmup steps:** 10
+- **Max sequence length:** 128
+### LoRA Configuration
+| Parameter | Value |
+|-----------|-------|
+| r (rank) | 8 |
+| lora_alpha | 16 |
+| target_modules | q_proj, v_proj |
+| lora_dropout | 0.05 |
+| bias | none |
+| task_type | CAUSAL_LM |
+## Technical Specifications
 ### Model Architecture and Objective
+LoRA adapter applied to the query and value projection layers of a tiny random LLaMA architecture for causal language modeling.
 ### Compute Infrastructure
 #### Hardware
+- Apple M3 Pro (36GB unified memory)
+- macOS Sequoia 15.6.1
 #### Software
+- Transformers
+- PEFT 0.18.0
+- PyTorch
+- Datasets
 ### Framework versions
+- PEFT 0.18.0