Use TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T as base_model_id

yuiseki · Mar 21, 2024 · 5747e5f · 5747e5f
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diff --git a/recipes/A5000_24GB_x8/color-coder.yaml b/recipes/A5000_24GB_x8/color-coder.yaml
@@ -1,5 +1,5 @@
 target_task: tasks/text-generation/text2rgbhex.md
-base_model_id: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+base_model_id: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_name: tinyllama-color-coder-v1
 output_base_dir: output
 dataset_id: burkelibbey/colors

diff --git a/recipes/A5000_24GB_x8/fake-news-detector.yaml b/recipes/A5000_24GB_x8/fake-news-detector.yaml
@@ -1,5 +1,5 @@
 target_task: tasks/nlp/fake-news-detection.md
-base_model_id: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+base_model_id: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_name: tinyllama-fake-news-detector-v1
 output_base_dir: output
 dataset_id: mrm8488/fake-news

diff --git a/recipes/A5000_24GB_x8/hate-speech-detector.yaml b/recipes/A5000_24GB_x8/hate-speech-detector.yaml
@@ -1,5 +1,5 @@
 target_task: tasks/nlp/hate-speech-detection.md
-base_model_id: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+base_model_id: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_name: tinyllama-hate-speech-detector-v1
 output_base_dir: output
 dataset_id: tweets_hate_speech_detection

diff --git a/recipes/A5000_24GB_x8/ja.yaml b/recipes/A5000_24GB_x8/ja.yaml
@@ -1,5 +1,5 @@
 target_task: tasks/nlp/hate-speech-detection.md
-base_model_id: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+base_model_id: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_name: tinyllama-ja-v1
 output_base_dir: output
 dataset_id: izumi-lab/llm-japanese-dataset-vanilla

diff --git a/recipes/A5000_24GB_x8/sentiment-analyzer.yaml b/recipes/A5000_24GB_x8/sentiment-analyzer.yaml
@@ -1,6 +1,6 @@
 target_task: tasks/nlp/hate-speech-detection.md
 base_model_id: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-model_name: tinyllama-sentiment-analyzer-v1
+model_name: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 output_base_dir: output
 dataset_id: carblacac/twitter-sentiment-analysis
 dataset_input_hint: Given the text, predict whether it is positive or negative. You must always, without fail, output just only with 0 or 1.