Upload model trained with Unsloth

Upload model trained with Unsloth 2x faster

README.md

@@ -8,21 +8,10 @@ tags:
 - trl
 license: apache-2.0
 language:
-- sw
-library_name: peft
-datasets:
-- Mollel/alpaca-swahili
-- Mollel/swahili_pretrain_data
-- wikimedia/wikipedia
+- en
 ---
 
-# Model Detauils
-
-
-This model has been pre-trained and fine-tuned specifically for Swahili language tasks. 
-The training includes 4-bit quantization to optimize performance on lower-resource hardware.
-
-This is a development version and it's not recommended for general use.
+# Uploaded  model
 
 - **Developed by:** calcpy
 - **License:** apache-2.0
@@ -31,88 +20,3 @@ This is a development version and it's not recommended for general use.
 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)
-
-
-### Out-of-Scope Use
-
-The model is not designed for tasks outside of the Swahili language or tasks requiring highly factual precision in domains not covered by the training datasets.
-
-
-## Bias, Risks, and Limitations
-
-The model inherits any potential biases present in the Swahili Wikipedia and Mollel's dataset. Users should be cautious when applying this model to sensitive applications.
-
-
-### Recommendations
-
-Users should perform bias evaluations specific to their use case and ensure that any downstream applications consider potential ethical implications.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load the model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("path_to_your_model")
-tokenizer = AutoTokenizer.from_pretrained("path_to_your_model")
-
-# Example inference
-instruction = "Endelea mlolongo wa fibonacci:"
-input_data = "1, 1, 2, 3, 5, 8,"
-prompt = f"Chini ni maagizo ambayo yanaelezea kazi. Andika jibu ambalo linakamilisha ombi ipasavyo.\n### Maagizo:\n{instruction}\n\n{input_data}\n### Jibu:\n"
-
-inputs = tokenizer([f"{prompt}"], return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
-print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
-```
-
-In this example, the model generates the continuation of the Fibonacci sequence in Swahili.
-
-
-## Training Details
-
-### Training Data
-
-The model was pre-trained using a combination of [Swahili Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) 
-and [Mollel’s Swahili pretraining dataset](https://huggingface.co/datasets/Mollel/swahili_pretrain_data). 
-Both datasets were processed to include End-of-Sequence (EOS) tokens and formatted for pretraining tasks.
-
-Finetuning was performed on [Mollel's Alpaca dataset](https://huggingface.co/datasets/Mollel/alpaca-swahili)
-
-### Training Procedure
-
-#### Training Hyperparameters
-
-- ** Training regime: Mixed precision (fp16/bf16)
-- ** Batch size: 2 per device
-- ** Max steps: 24,000 for pretraining, 1,200 for fine-tuning
-- ** Learning rate: 5e-5 (1e-5 for embeddings)
-- ** Warmup steps: 100 for pretraining, 10 for fine-tuning
-- ** Weight decay: 0.01 (pretraining), 0.00 (fine-tuning)
-
-## Evaluation
-
-The model was only manually evaluated on the Alpaca Swahili dataset for instruction-following capabilities.
-
-#### Metrics
-
-Evaluation metrics will be required for language generation quality and instruction-following precision
-
-#### Summary
-
-This is a purely technical release for a small test model in order to test pre-training and fine-tuning code on a single GPU. 
-
-
-## Environmental Impact
-
-- **Hardware Type:** NVIDIA GeForce RTX 4090 24 GiB
-- **Hours used:** ~12 hours
-
-
-### Compute Infrastructure
-
-Ubuntu 22.04.5 LTS with multiple NVIDIA GeForce RTX 4090 cards
-
-Only a single GPU unit was used

adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/llama-3.2-3b-instruct-bnb-4bit",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "lm_head",
+    "embed_tokens"
+  ],
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "q_proj",
+    "up_proj",
+    "v_proj",
+    "gate_proj",
+    "k_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": true
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
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