rank1-mistral-2501-24b

Name: rank1-mistral-2501-24b
Author: jhu-clsp

1 language

license:mit

jhu-clsp

Language Model

OTHER

24B params

New

1 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

54GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

23GB+ RAM

Code Examples

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

MTEB Integrationpython

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-mistral-2501-24b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.