---
title: "llama3-chatqa-1.5-8b"
publisher: "nvidia"
type: "endpoint"
updated: "2025-05-22T19:14:38.290Z"
description: "Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines."
canonical: "https://build.nvidia.com/nvidia/chatqa-1-5-8b"
---
# Llama3-ChatQA-1.5-8B Model card
## Model Information
### Model Summary
**Author:** NVIDIA
#### Description
Llama3-ChatQA-1.5 excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from ChatQA paper, and it is built on top of Llama-3 base model. Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B.
#### Terms of Use
By accessing this model, you are agreeing to the [NVIDIA AI Foundation Models Community License](https://developer.download.nvidia.com/ai-foundation-models/nvidia-ai-foundation-models-license-10Nov2023.pdf)
Additional Information: [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/).
#### Reference:
```none
@article{liu2024chatqa,
title={ChatQA: Surpassing GPT-4 on Conversational QA and RAG},
author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
journal={arXiv preprint arXiv:2401.10225},
year={2024}}
```
#### Resources and Technical Documentation
* [ChatQA Project Page](https://chatqa-project.github.io/)
* [Technical Report](https://arxiv.org/pdf/2401.10225)
#### Model Architecture:
**Architecture Type:** Transformer Decoder Network
**Network Architecture:** Llama-3
#### Inputs and outputs
## Input:
**Input Type(s):** Text
**Input Format(s):** String
**Input Parameters:** One-Dimensional (1D)
## Output:
**Output Type(s):** Text
**Output Format(s):** String
**Output Parameters:** One-Dimensional (1D)
## Ethical Considerations (For NVIDIA Models Only):
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
## Bias
Field | Response
:---------------------------------------------------------------------------------------------------|:---------------
Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None
Measures taken to mitigate against unwanted bias: | None
## Explainability
Field | Response
:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
Intended Application & Domain: | Chat, Retrieval-Augmented Generation (RAG)
Model Type: | Transformer
Intended Users: | Generative AI creators working with conversational AI models.
Output: | Text (Responds to posed question, Stateful - remembers previous answers)
Describe how the model works: | Text tokens are passed into transformer-based language model and output as a text response.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable
Technical Limitations: | None
Verified to have met prescribed NVIDIA quality standards: | Yes
Performance Metrics: | Accuracy (Chat, RAG), Latency, Throughput
Potential Known Risks: | None Known
Licensing: | [NVIDIA AI Foundation Models Community License](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)
## Privacy
Field | Response
:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
Generatable or reverse engineerable personally-identifiable information (PII)? | None
Protected class data used to create this model? | None
Was consent obtained for any PII used? | Not Applicable
How often is dataset reviewed? | Before Every Release
Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable
If PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable
If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable
If PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable
Is there provenance for all datasets used in training? | Yes
Does data labeling (annotation, metadata) comply with privacy laws? | Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.
## Safety & Security
Field | Response
:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
Generatable or reverse engineerable personally-identifiable information (PII)? | None
Protected class data used to create this model? | None
Was consent obtained for any PII used? | Not Applicable
How often is dataset reviewed? | Before Every Release
Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable
If PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable
If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable
If PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable
Is there provenance for all datasets used in training? | Yes
Does data labeling (annotation, metadata) comply with privacy laws? | Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.
## Prototype
```python
from openai import OpenAI
client = OpenAI(
base_url = "https://integrate.api.nvidia.com/v1",
api_key = "$NVIDIA_API_KEY"
)
completion = client.chat.completions.create(
model="",
messages=[{"role":"user","content":""}],
temperature=,
top_p=,
max_tokens=,
stream=NaN
)
print(completion.choices[0].message)
```
```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA
client = ChatNVIDIA(
model="",
api_key="$NVIDIA_API_KEY",
temperature=,
top_p=,
max_tokens=,
)
response = client.invoke([{"role":"user","content":""}])
print(response.content)
```
```javascript
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: '$NVIDIA_API_KEY',
baseURL: 'https://integrate.api.nvidia.com/v1',
})
async function main() {
const completion = await openai.chat.completions.create({
model: "",
messages: [{"role":"user","content":""}],
temperature: ,
top_p: ,
max_tokens: ,
stream: ,
})
process.stdout.write(completion.choices[0]?.message?.content);
}
main();
```
```bash
curl https://integrate.api.nvidia.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $NVIDIA_API_KEY" \
-d '{
"model": "nvidia/llama3-chatqa-1.5-8b",
"messages": [{"role":"user","content":""}],
"temperature": ,
"top_p": ,
"max_tokens": ,
"stream":
}'
```