Un servidor MCP para Unsloth : una biblioteca que hace que el ajuste de LLM sea dos veces más rápido con un 80 % menos de memoria.
Unsloth es una biblioteca que mejora drásticamente la eficiencia del ajuste de modelos de lenguaje grandes:
Velocidad : ajuste fino 2 veces más rápido en comparación con los métodos estándar
Memoria : 80 % menos de uso de VRAM, lo que permite ajustar modelos más grandes en GPU de consumo
Longitud del contexto : longitudes de contexto hasta 13 veces más largas (por ejemplo, 89 000 tokens para Llama 3.3 en GPU de 80 GB)
Precisión : Sin pérdida de calidad ni rendimiento del modelo.
Unsloth logra estas mejoras a través de kernels CUDA personalizados escritos en el lenguaje Triton de OpenAI, retropropagación optimizada y cuantificación dinámica de 4 bits.
Related MCP server: TOON MCP Server
Optimizar el ajuste fino para Llama, Mistral, Phi, Gemma y otros modelos
Cuantización de 4 bits para un entrenamiento eficiente
Soporte de longitud de contexto extendida
API simple para carga, ajuste e inferencia de modelos
Exportar a varios formatos (GGUF, Hugging Face, etc.)
Instalar Unsloth: pip install unsloth
Instalar y construir el servidor:
cd unsloth-server
npm install
npm run buildAgregar a la configuración de MCP:
{
"mcpServers": {
"unsloth-server": {
"command": "node",
"args": ["/path/to/unsloth-server/build/index.js"],
"env": {
"HUGGINGFACE_TOKEN": "your_token_here" // Optional
},
"disabled": false,
"autoApprove": []
}
}
}Verifique si Unsloth está instalado correctamente en su sistema.
Parámetros : Ninguno
Ejemplo :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "check_installation",
arguments: {}
});Obtén una lista de todos los modelos compatibles con Unsloth, incluidas las variantes Llama, Mistral, Phi y Gemma.
Parámetros : Ninguno
Ejemplo :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "list_supported_models",
arguments: {}
});Cargue un modelo previamente entrenado con optimizaciones de Unsloth para una inferencia y un ajuste más rápidos.
Parámetros :
model_name (obligatorio): Nombre del modelo a cargar (p. ej., "unsloth/Llama-3.2-1B")
max_seq_length (opcional): Longitud máxima de secuencia para el modelo (predeterminado: 2048)
load_in_4bit (opcional): si se debe cargar el modelo en cuantificación de 4 bits (valor predeterminado: verdadero)
use_gradient_checkpointing (opcional): si se deben utilizar puntos de control de gradiente para ahorrar memoria (valor predeterminado: verdadero)
Ejemplo :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "load_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
max_seq_length: 4096,
load_in_4bit: true
}
});Ajuste un modelo con optimizaciones de Unsloth utilizando técnicas LoRA/QLoRA.
Parámetros :
model_name (obligatorio): Nombre del modelo a ajustar
dataset_name (obligatorio): Nombre del conjunto de datos que se utilizará para el ajuste fino
output_dir (obligatorio): Directorio para guardar el modelo ajustado
max_seq_length (opcional): Longitud máxima de secuencia para entrenamiento (predeterminado: 2048)
lora_rank (opcional): rango para el ajuste fino de LoRA (predeterminado: 16)
lora_alpha (opcional): Alfa para el ajuste fino de LoRA (valor predeterminado: 16)
batch_size (opcional): tamaño del lote para entrenamiento (predeterminado: 2)
gradient_accumulation_steps (opcional): Número de pasos de acumulación de gradiente (predeterminado: 4)
learning_rate (opcional): tasa de aprendizaje para el entrenamiento (predeterminado: 2e-4)
max_steps (opcional): Número máximo de pasos de entrenamiento (predeterminado: 100)
dataset_text_field (opcional): Campo en el conjunto de datos que contiene el texto (predeterminado: 'texto')
load_in_4bit (opcional): si se debe utilizar cuantificación de 4 bits (valor predeterminado: verdadero)
Ejemplo :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "finetune_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
dataset_name: "tatsu-lab/alpaca",
output_dir: "./fine-tuned-model",
max_steps: 100,
batch_size: 2,
learning_rate: 2e-4
}
});Genere texto utilizando un modelo Unsloth perfeccionado.
Parámetros :
model_path (obligatorio): Ruta al modelo ajustado
prompt (obligatorio): Solicitud de generación de texto
max_new_tokens (opcional): Número máximo de tokens a generar (predeterminado: 256)
temperature (opcional): Temperatura para la generación de texto (predeterminado: 0,7)
top_p (opcional): Top-p para generación de texto (predeterminado: 0.9)
Ejemplo :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "generate_text",
arguments: {
model_path: "./fine-tuned-model",
prompt: "Write a short story about a robot learning to paint:",
max_new_tokens: 512,
temperature: 0.8
}
});Exporte un modelo Unsloth ajustado a varios formatos para su implementación.
Parámetros :
model_path (obligatorio): Ruta al modelo ajustado
export_format (obligatorio): Formato al que se exportará (gguf, ollama, vllm, huggingface)
output_path (obligatorio): Ruta para guardar el modelo exportado
quantization_bits (opcional): Bits para cuantificación (para exportación GGUF) (valor predeterminado: 4)
Ejemplo :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "export_model",
arguments: {
model_path: "./fine-tuned-model",
export_format: "gguf",
output_path: "./exported-model.gguf",
quantization_bits: 4
}
});Puede utilizar conjuntos de datos personalizados formateándolos correctamente y alojándolos en Hugging Face o proporcionando una ruta local:
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "finetune_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
dataset_name: "json",
data_files: {"train": "path/to/your/data.json"},
output_dir: "./fine-tuned-model"
}
});Para modelos grandes con hardware limitado:
Reducir el tamaño del lote y aumentar los pasos de acumulación de gradiente
Utilice cuantificación de 4 bits
Habilitar puntos de control de gradiente
Reducir la longitud de la secuencia si es posible
CUDA sin memoria : reduzca el tamaño del lote, utilice cuantificación de 4 bits o pruebe un modelo más pequeño
Errores de importación : asegúrese de tener instaladas las versiones correctas de Torch, Transformers y Unsloth.
Modelo no encontrado : comprueba que estás usando un nombre de modelo compatible o que tienes acceso a modelos privados
Python: 3.10, 3.11 o 3.12 (no 3.13)
CUDA: se recomienda 11.8 o 12.1+
PyTorch: se recomienda la versión 2.0+
Modelo | VRAM | Velocidad sin pereza | Reducción de VRAM | Longitud del contexto |
Llama 3.3 (70B) | 80 GB | 2 veces más rápido | >75% | 13 veces más largo |
Llama 3.1 (8B) | 80 GB | 2 veces más rápido | >70% | 12 veces más largo |
Mistral v0.3 (7B) | 80 GB | 2,2 veces más rápido | 75% menos | - |
Python 3.10-3.12
GPU NVIDIA con soporte CUDA (recomendado)
Node.js y npm
Apache-2.0
Check if Unsloth is properly installed
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe what 'properly installed' means, what checks are performed, what output format to expect, or whether this has side effects. For a diagnostic tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple diagnostic tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter diagnostic tool with no output schema, the description states the basic purpose adequately. However, it doesn't explain what constitutes 'properly installed' or what format the result will take, leaving the agent uncertain about how to interpret the tool's output. Given the simplicity of the tool, this is minimally viable but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: checking if Unsloth is properly installed. It uses a specific verb ('check') and identifies the target resource ('Unsloth installation'). However, it doesn't differentiate from siblings like 'list_supported_models' which might also involve installation status checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or relationships to sibling tools like 'list_supported_models' or 'load_model' that might be used before or after installation checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export a fine-tuned Unsloth model to various formats
| Name | Required | Description | Default |
|---|---|---|---|
| model_path | Yes | Path to the fine-tuned model | |
| export_format | Yes | Format to export to (gguf, ollama, vllm, huggingface) | |
| output_path | Yes | Path to save the exported model | |
| quantization_bits | No | Bits for quantization (for GGUF export) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool exports models but doesn't describe what the export entails (e.g., file creation, format conversion, potential data loss, permissions required, or rate limits). For a tool with 4 parameters and no annotations, this leaves significant gaps in understanding its behavior and side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Export a fine-tuned Unsloth model') and adds necessary detail ('to various formats'). There is no wasted verbiage, and it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with full schema coverage but no annotations and no output schema, the description is minimally adequate. It covers the basic purpose but lacks behavioral context, usage guidelines, and output details. For a tool that likely involves file operations and format conversions, more completeness would be beneficial, but it meets a bare minimum.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'various formats,' which aligns with the 'export_format' enum but doesn't provide additional syntax or usage details. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Export') and resource ('a fine-tuned Unsloth model'), specifying the target formats ('various formats'). It distinguishes from siblings like 'finetune_model' or 'load_model' by focusing on export rather than creation or loading. However, it doesn't explicitly differentiate from all siblings (e.g., 'generate_text' is clearly different, but the distinction is implicit rather than explicit).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a fine-tuned model first), exclusions (e.g., not for raw models), or comparisons to sibling tools like 'list_supported_models' for checking export options. Usage is implied by the action but lacks explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fine-tune a model with Unsloth optimizations
| Name | Required | Description | Default |
|---|---|---|---|
| model_name | Yes | Name of the model to fine-tune | |
| dataset_name | Yes | Name of the dataset to use for fine-tuning | |
| output_dir | Yes | Directory to save the fine-tuned model | |
| max_seq_length | No | Maximum sequence length for training | |
| lora_rank | No | Rank for LoRA fine-tuning | |
| lora_alpha | No | Alpha for LoRA fine-tuning | |
| batch_size | No | Batch size for training | |
| gradient_accumulation_steps | No | Number of gradient accumulation steps | |
| learning_rate | No | Learning rate for training | |
| max_steps | No | Maximum number of training steps | |
| dataset_text_field | No | Field in the dataset containing the text | |
| load_in_4bit | No | Whether to use 4-bit quantization |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Unsloth optimizations' but doesn't explain what this entails (e.g., performance improvements, memory efficiency). Critical behavioral traits like computational cost, time requirements, potential data destruction, or output format are omitted. The description adds minimal context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded with the core action ('fine-tune a model') and includes a relevant detail ('with Unsloth optimizations') that adds value without verbosity. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a fine-tuning operation (12 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral aspects (e.g., runtime, resource usage), output (what is returned), and usage guidelines. The high parameter count and absence of structured support fields make this inadequate for such a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 12 parameters thoroughly (e.g., 'model_name', 'dataset_name', 'lora_rank'). The description adds no additional meaning about parameters, such as typical values, constraints, or relationships between them. Baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('fine-tune') and resource ('a model') with a specific optimization method ('with Unsloth optimizations'). It distinguishes from siblings like 'export_model' or 'generate_text' by focusing on training rather than inference or export. However, it doesn't explicitly differentiate from 'load_model' in terms of when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a pre-trained model and dataset), when not to use it (e.g., for inference tasks), or refer to sibling tools like 'list_supported_models' for model selection. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate text using a fine-tuned Unsloth model
| Name | Required | Description | Default |
|---|---|---|---|
| model_path | Yes | Path to the fine-tuned model | |
| prompt | Yes | Prompt for text generation | |
| max_new_tokens | No | Maximum number of tokens to generate | |
| temperature | No | Temperature for text generation | |
| top_p | No | Top-p for text generation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only states the basic function without disclosing behavioral traits like computational cost, rate limits, error handling, or output format. It mentions 'fine-tuned Unsloth model' but doesn't explain implications for performance or compatibility.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every part contributing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a text generation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It fails to address key aspects like what the output looks like, error conditions, or how it integrates with sibling tools (e.g., requiring 'load_model' first).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no additional meaning beyond what's in the schema, such as typical values for 'temperature' or 'top_p', or how 'model_path' relates to other tools. Baseline 3 is appropriate as the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate text') and specifies the resource ('using a fine-tuned Unsloth model'), which distinguishes it from siblings like 'finetune_model' or 'export_model'. However, it doesn't explicitly differentiate from 'load_model' in terms of when to use each for text generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'load_model' (which might be a prerequisite) or other text generation methods. The description lacks context about prerequisites, such as needing a loaded model, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all models supported by Unsloth
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states what the tool does but lacks behavioral details such as whether it requires authentication, has rate limits, returns paginated results, or what format the output takes. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks context on output format, behavioral traits, or usage guidelines. This makes it complete enough for basic understanding but with clear gaps in practical application.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied as per the rules for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all models supported by Unsloth'), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'check_installation' or 'load_model' which might also involve model information, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Sibling tools like 'check_installation' or 'load_model' might overlap in context, but the description doesn't mention any prerequisites, exclusions, or specific scenarios for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a pretrained model with Unsloth optimizations
| Name | Required | Description | Default |
|---|---|---|---|
| model_name | Yes | Name of the model to load (e.g., "unsloth/Llama-3.2-1B") | |
| max_seq_length | No | Maximum sequence length for the model | |
| load_in_4bit | No | Whether to load the model in 4-bit quantization | |
| use_gradient_checkpointing | No | Whether to use gradient checkpointing to save memory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Unsloth optimizations' but doesn't explain what this entails (e.g., memory efficiency, speed improvements) or potential side effects (e.g., memory usage, time to load, compatibility issues). It lacks details on error handling, performance implications, or what 'load' means operationally (e.g., into memory for subsequent use).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core action and key detail ('Unsloth optimizations'), making it easy to parse. Every word earns its place without redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (loading models with optimizations), lack of annotations, and no output schema, the description is incomplete. It doesn't cover what happens after loading (e.g., model availability for other tools), potential errors, or the impact of Unsloth optimizations. For a tool with 4 parameters and no structured safety or output info, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify the purpose of 'max_seq_length' or trade-offs for 'load_in_4bit'). Baseline score of 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Load') and resource ('a pretrained model'), and specifies the optimization framework ('with Unsloth optimizations'). It distinguishes from siblings like 'finetune_model' (training) and 'list_supported_models' (listing), but doesn't explicitly differentiate from 'export_model' or 'generate_text' in terms of loading vs. using the model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., after checking installation with 'check_installation'), when loading is needed (e.g., before fine-tuning or generation), or what happens if the model is already loaded. The description assumes context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
check_installationexport_modelfinetune_modelgenerate_textlist_supported_modelsload_modelScored across 6 tools
Each tool has a clearly distinct purpose with no overlap: checking installation, listing models, loading models, fine-tuning, generating text, and exporting models. The descriptions make it unambiguous which tool to use for each step in the workflow.
All tools follow a consistent verb_noun pattern with clear, descriptive names (e.g., check_installation, export_model, finetune_model). There are no deviations in naming conventions or style mixing.
With 6 tools, this server is well-scoped for its purpose of fine-tuning and using models with Unsloth optimizations. Each tool earns its place by covering a distinct, essential operation in the model lifecycle.
The tool set provides complete coverage for the domain: from checking prerequisites and listing models, through loading, fine-tuning, and generating text, to exporting the final model. There are no obvious gaps or dead ends in the workflow.
LLM chat, text tools, image generation, editing, batch image jobs, and asynchronous video generation
Image, video, chat and speech models with one account — local (npx) or hosted with OAuth sign-in.
Turn any LLM multimodal; generate images, voices, videos, 3D models, music, and more.
Provision private AI model endpoints on dedicated GPUs (Llama, Qwen, Mistral). Pay per minute.
