> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Models
export const GCell = ({children, className}) =>
{children}
;
export const GTH = ({children, className}) =>
{children}
;
export const GRow = ({children}) => {children}
;
export const GBody = ({children}) => {children}
;
export const GHead = ({children}) => {children}
;
export const GTable = ({children, className, cols}) =>
{children}
;
The `.models` accessor provides methods for listing and downloading models.
Method
Return type
Description
`.list()`
`List[Model]`
List all available models
`.get('')`
`Model`
Fetch model metadata and quantizations
`.download('/')`
`None`
Download a quantized model file
`.delete('/')`
`None`
Delete a downloaded model file
## Model class
Attribute/Method
Type
Description
`.name`
`str`
Model name
`.description`
`str`
Model description
`.num_parameters`
`int`
Number of model parameters
`.trained_for`
`str`
Training purpose (for example, 'text-generation', 'feature-extraction')
`.context_window_size`
`int`
Context window length
`.quantized_files`
`List[QuantizedFile]`
Available quantization files
`.get_quantization('')`
`QuantizedFile`
Get a specific quantization by method
`.download('')`
`None`
Download a specific quantization
`.delete('')`
`None`
Delete a specific quantization
## QuantizedFile class
Attribute/Method
Type
Description
`.sha256`
`str`
SHA256 checksum of the model file
`.size_bytes`
`int`
File size in bytes
`.quant_method`
`str`
Quantization method (for example, 'Q4\_K\_M', 'Q8\_0')
`.max_ram_usage`
`int`
Required RAM in bytes
`.format`
`str`
File format (typically 'gguf')
`.identifier`
`str`
Full filename (for example, 'model\_name\_q4\_k\_m.gguf')
`.is_downloaded`
`bool`
Whether the file is downloaded
`.is_allowed`
`bool`
Whether the file is available on this system
`.local_path`
`Optional[Path]`
Local file path (if downloaded)
`.download()`
`None`
Download this quantization file
`.delete()`
`None`
Delete this quantization file
## Downloading models
Download a quantized model file using one of the following approaches:
```py theme={null}
client.models.download('OpenHermes-2.5-Mistral-7B/Q4_K_M')
```
`client.models.download()` accepts either a string reference (`'model/quantization'`) or a `QuantizedFile` object.
***
If the model has already been downloaded, the function exits immediately. Otherwise, a progress bar displays.
```py theme={null}
model = client.models.get('OpenHermes-2.5-Mistral-7B')
model.download('Q4_K_M')
```
```py theme={null}
model = client.models.get('OpenHermes-2.5-Mistral-7B')
quantization = model.get_quantization('Q4_K_M')
quantization.download()
```
## Example usage
```py theme={null}
from anaconda_ai import AnacondaAIClient
client = AnacondaAIClient()
# List all models
models = client.models.list()
for model in models:
print(f"{model.name}: {model.num_parameters / 1e9:.1f}B parameters")
# Get specific model details
model = client.models.get('OpenHermes-2.5-Mistral-7B')
print(f"Description: {model.description}")
print(f"Trained for: {model.trained_for}")
# Check available quantizations
for quant in model.quantized_files:
status = "downloaded" if quant.is_downloaded else "available"
print(f" {quant.quant_method}: {status} ({quant.size_bytes / 1e9:.1f}GB)")
# Download and use a quantization
if not model.get_quantization('Q4_K_M').is_downloaded:
model.download('Q4_K_M')
quantization = model.get_quantization('Q4_K_M')
print(f"Local path: {quantization.local_path}")
```