abacusai
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Llama-3-Giraffe-70B-Instruct

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 ---
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- meta
+- llama-3
+license: llama3
 ---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/VcZWbW_eZkJAZZ5ricL4B.png)
+# Llama-3-Giraffe-70B-Instruct
+Abacus.AI presents our longer-necked variant of Llama 3 70B - now with the instruct variant!
+This model has an effective context length of approximately 128k.
+We have currently trained on ~1.5B tokens.
+There are our Needle-in-a-Haystack heatmap results. We are conducting further evals of model efficacy and will update our model card as these come in:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/Z4uUhcjgf1P7EPGQyRLkW.png)
+## Training Methodology
+The methodology for training uses [PoSE](https://arxiv.org/abs/2309.10400) and dynamic-NTK interpolation.
+### NTK-scaling
+The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments.
+### PoSE
+We utilise Positional Skip-wise Training (PoSE) with the following parameters:
+- **Number of Chunks**: 5
+- **Max position ID**: 32768
+### Data
+We use on average ~8K long samples from [RedPajama](https://github.com/togethercomputer/RedPajama-Data).
+### Hardware
+We train on 8xH100 GPUs with Deepspeed Zero Stage 3.
+## Evaluation Methodology
+We use the [EasyContext](https://github.com/abacusai/EasyContext/blob/eval_runs/eval_needle.py) implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B.
+We evaluate with the following parameters:
+- **Min context length**: 2000
+- **Max context length**: 128000
+- **Context interval**: 4000
+- **Depth interval**: 0.1
+- **Num samples**: 2
+- **Rnd number digits**: 7
+- **Haystack dir**: PaulGrahamEssays
+### Adapter Transfer
+We apply the above techniques first to Llama-3-70B-Base, using LoRA on the Q and K weights only. This adapter is then applied to Llama-3-70B-Instruct, and we
+release the merged version here.