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add: link to mistral + typo

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hahuyhoang411 2024-03-01 14:58:37 +07:00
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@ -31,7 +31,7 @@ Problems still arise with catastrophic forgetting in general tasks, commonly obs
## Selecting a strong foundation model ## Selecting a strong foundation model
Mistral 7B continues to outshine [Meta's Llama-2 7B](https://huggingface.co/meta-llama/Llama-2-7b) and [Google's Gemma 7B](https://huggingface.co/google/gemma-7b) on meaningful benchmarks, so we selected this as a starting point. [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) continues to outshine [Meta's Llama-2 7B](https://huggingface.co/meta-llama/Llama-2-7b) and [Google's Gemma 7B](https://huggingface.co/google/gemma-7b) on meaningful benchmarks, so we selected this as a starting point.
Having a robust base model is critical. In our experiments, using Mistral as a starting point ensured the highest accuracy for subsequent specialized adaptations. Having a robust base model is critical. In our experiments, using Mistral as a starting point ensured the highest accuracy for subsequent specialized adaptations.
@ -51,7 +51,7 @@ Mistral alone has known, poor math capabilities, which we needed for our highly
We found model merging to be a viable approach where each iteration is cost-effective + fast to deploy. We found model merging to be a viable approach where each iteration is cost-effective + fast to deploy.
We ended up with [Stealth v1.1](https://huggingface.co/jan-hq/stealth-v1.1), a [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) merge of Mistral with the following: We ended up with [Stealth 7B v1.1](https://huggingface.co/jan-hq/stealth-v1.1), a [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) merge of Mistral with the following:
- [WizardMath](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) for its math capabilities - [WizardMath](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) for its math capabilities
- [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0) for its coding capabilities - [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0) for its coding capabilities
@ -65,7 +65,7 @@ Merging different LLMs can lead to the mixed answering style because each model
Thus, we applied Direct Preference Optimization ([DPO](https://arxiv.org/abs/2305.18290)) using the [Intel's Orca DPO pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) dataset, chosen for its helpful answering style in general, math and coding concentration. Thus, we applied Direct Preference Optimization ([DPO](https://arxiv.org/abs/2305.18290)) using the [Intel's Orca DPO pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) dataset, chosen for its helpful answering style in general, math and coding concentration.
This approach result in a final model - [Stealth v1.2](https://huggingface.co/jan-hq/stealth-v1.2), with minimal loss, and realign to our technical preferences. This approach result in a final model - [Stealth 7B v1.2](https://huggingface.co/jan-hq/stealth-v1.2), with minimal loss, and realign to our technical preferences.
## **Using our own technical documentation** ## **Using our own technical documentation**