Models
Model Milestones
- Intern-S1: Our latest open-source multimodal large model enhanced with advanced scientific reasoning capabilities. It currently delivers the best overall performance among all open-source multimodal models.
- InternLM3: Our latest series of large language models with exceptional reasoning capabilities, leading the open-source models in this class.
- InternVL2.5: Our multimodal large model series, the first open-source model to match the performance of top proprietary models like GPT-4 and Gemini Pro.
Using the Latest Model
For multi-turn conversation APIs, use the following in the model
field:
intern-latest
automatically refers to our most recent model release, which currently points tointern-s1
.internlm3-latest
will automatically point to the latest version in the large language model series.
You can check the specific model version they point to by checking the ref_model
field in the response from the models API (e.g., internlm3-8b-instruct
).
Model List
Multimodal Models
Model | Description | Context Length |
---|---|---|
intern-latest | Alias for our most recently released Intern-series model, currently pointing to intern-s1 | 32K |
intern-s1 | Our latest multimodal model with strong scientific reasoning, offering top-tier performance | 32K |
internvl-latest | Points to our latest release in the InternVL series, currently internvl3-78b | 32K |
internvl3-78b | Our most recent multimodal model, featuring enhanced image-text understanding and long-horizon visual reasoning. Its performance rivals leading proprietary models | 32K |
Large Language Models
Model | Description | Window Length |
---|---|---|
internlm3-latest | Default points to the latest released InternLM3 model series, currently pointing to internlm3-8b-instruct | 32K |
internlm3-8b-instruct | Our latest model with leading reasoning capabilities in its scale | 32K |
internlm2.5-latest | Default points to the latest released InternLM2.5 model series, currently pointing to internlm2.5-20b-chat | 32K |
Request Examples
(1) Python Example
import requests
import json
url = 'https://chat.intern-ai.org.cn/api/v1/models'
header = {
'Content-Type': 'application/json',
"Authorization": "Bearer eyJ0eXBlIjoiSl...Please provide the correct token!"
}
data = {}
res = requests.get(url, headers=header, data=json.dumps(data))
print(res.status_code)
print(res.json())
print(res.json()["data"])
(2) OpenAI SDK Example
from openai import OpenAI
client = OpenAI(
api_key="eyJ0eXBlIjoiSl...Please provide the correct token!", # Provide token here, without 'Bearer'
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
models = client.models.list()
for model in models:
print(model)
(3) OpenAI CLI Example
openai -b "https://chat.intern-ai.org.cn/api/v1/"
-k "eyJ0eXBlIjoiSl...Please provide the correct token!"
api models.list
Parameter Explanation
Return Parameters
Parameter | Type | Example | Description |
---|---|---|---|
object | string | list | Data type returned, in this case, "list" |
data | object | { "id": "intern-latest", "object": "model", "created": 1686935002, "owned_by": "pjlab"} | Model information |
Relevant Structure Explanation
- Data Structure Explanation
Parameter | Type | Example | Description |
---|---|---|---|
id | string | intern-latest | Model ID |
object | string | model | Model category, always "model" |
created | int | 1677652288 | Model creation timestamp |
owned_by | string | SH-AILab | Model owner, default is "SH-AILab" |
// Request
Schema: HTTP
Path: /api/v1/models
Method: GET
Header:
sso-ak: xxxxxx
Authorization: $BearerToken
// Response
Status Code: 200
Body:
{
"object": "list",
"data": [
{
"id": "intern-latest",
"object": "model",
"ref_model": "intern-s1",
"created": 1722424576,
"owned_by": "SH-AILab"
},
{
"id": "internvl3-latest",
"object": "model",
"ref_model": "internvl3-78b",
"created": 1722424576,
"owned_by": "SH-AILab"
},
{
"id": "internlm3-latest",
"object": "model",
"ref_model": "internlm2.5-20b-0719",
"created": 1722424576,
"owned_by": "SH-AILab"
}
]
}