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---
language:
- en
license: other
library_name: transformers
tags:
- pytorch
- llama
- llama-2
- qCammel-70
pipeline_tag: text-generation
inference: false
model-index:
- name: qCammel-70
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.34
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=augtoma/qCammel-70
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.87
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=augtoma/qCammel-70
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 70.18
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=augtoma/qCammel-70
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.47
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=augtoma/qCammel-70
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 84.29
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=augtoma/qCammel-70
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 29.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=augtoma/qCammel-70
name: Open LLM Leaderboard
---
# qCammel-70
qCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept their License before downloading this model .*
The fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA,
**Variations** The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 70B model.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved
**Research Papers**
- [Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://arxiv.org/abs/2305.12031)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)
- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.70971)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70)
| Metric |Value|
|---------------------------------|----:|
|Avg. |66.31|
|AI2 Reasoning Challenge (25-Shot)|68.34|
|HellaSwag (10-Shot) |87.87|
|MMLU (5-Shot) |70.18|
|TruthfulQA (0-shot) |57.47|
|Winogrande (5-shot) |84.29|
|GSM8k (5-shot) |29.72|
|