update some description and implement LlamaCpp
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anovazzi1
parent
7617f340f0
commit
0dbd1763f3
11 changed files with 149 additions and 12 deletions
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@ -8,7 +8,7 @@ from langflow import CustomComponent
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class AmazonBedrockComponent(CustomComponent):
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display_name: str = "Amazon Bedrock model"
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description: str = "LLM model from Amazon Bedrock."
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description: str = "Generate text using LLM model from Amazon Bedrock."
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def build_config(self):
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return {
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@ -10,7 +10,7 @@ from langflow import CustomComponent
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class AnthropicLLM(CustomComponent):
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display_name: str = "Anthropic model"
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description: str = "Anthropic Chat&Completion large language models."
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description: str = "Generate text using Anthropic Chat&Completion large language models."
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def build_config(self):
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return {
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@ -6,7 +6,7 @@ from langchain_openai import AzureChatOpenAI
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class AzureChatOpenAIComponent(CustomComponent):
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display_name: str = "AzureOpenAI model"
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description: str = "LLM model from Azure OpenAI."
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description: str = "Generate text using LLM model from Azure OpenAI."
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documentation: str = (
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"https://python.langchain.com/docs/integrations/llms/azure_openai"
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)
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@ -10,7 +10,7 @@ from langflow.field_typing import Text
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class QianfanChatEndpointComponent(CustomComponent):
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display_name: str = "QianfanChat Model"
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description: str = (
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"Baidu Qianfan chat models. Get more detail from "
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"Generate text using Baidu Qianfan chat models. Get more detail from "
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"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
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)
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@ -8,7 +8,7 @@ from langflow import CustomComponent
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class CTransformersComponent(CustomComponent):
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display_name = "CTransformers model"
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description = "C Transformers LLM models"
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description = "Generate text using CTransformers LLM models"
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documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
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def build_config(self):
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@ -34,5 +34,6 @@ class CTransformersComponent(CustomComponent):
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def build(self, model: str, model_file: str,inputs:str, model_type: str, config: Optional[Dict] = None) -> Text:
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output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)
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message = output.invoke(inputs)
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self.status = message
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return message
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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@ -5,7 +5,7 @@ from langflow.field_typing import Text
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class CohereComponent(CustomComponent):
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display_name = "Cohere model"
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description = "Cohere large language models."
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description = "Generate text using Cohere large language models."
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documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
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def build_config(self):
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@ -9,7 +9,7 @@ from langflow.field_typing import Text
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class GoogleGenerativeAIComponent(CustomComponent):
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display_name: str = "Google Generative AI model"
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description: str = "A component that uses Google Generative AI to generate text."
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description: str = "Generate text using Google Generative AI to generate text."
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documentation: str = "http://docs.langflow.org/components/custom"
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def build_config(self):
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@ -7,7 +7,7 @@ from langflow.field_typing import Text
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class HuggingFaceEndpointsComponent(CustomComponent):
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display_name: str = "Hugging Face Inference API models"
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description: str = "LLM model from Hugging Face Inference API."
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description: str = "Generate text using LLM model from Hugging Face Inference API."
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def build_config(self):
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return {
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136
src/backend/langflow/components/models/LlamaCpp.py
Normal file
136
src/backend/langflow/components/models/LlamaCpp.py
Normal file
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@ -0,0 +1,136 @@
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from typing import Optional, List, Dict, Any
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from langflow import CustomComponent
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from langchain_community.llms.llamacpp import LlamaCpp
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from langflow.field_typing import Text
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class LlamaCppComponent(CustomComponent):
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display_name = "LlamaCpp model"
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description = "Generate text using llama.cpp model."
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documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
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def build_config(self):
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return {
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"grammar": {"display_name": "Grammar", "advanced": True},
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"cache": {"display_name": "Cache", "advanced": True},
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"client": {"display_name": "Client", "advanced": True},
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"echo": {"display_name": "Echo", "advanced": True},
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"f16_kv": {"display_name": "F16 KV", "advanced": True},
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"grammar_path": {"display_name": "Grammar Path", "advanced": True},
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"last_n_tokens_size": {"display_name": "Last N Tokens Size", "advanced": True},
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"logits_all": {"display_name": "Logits All", "advanced": True},
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"logprobs": {"display_name": "Logprobs", "advanced": True},
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"lora_base": {"display_name": "Lora Base", "advanced": True},
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"lora_path": {"display_name": "Lora Path", "advanced": True},
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"max_tokens": {"display_name": "Max Tokens", "advanced": True},
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"metadata": {"display_name": "Metadata", "advanced": True},
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"model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
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"model_path": {
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"display_name": "Model Path",
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"field_type": "file",
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"file_types": [".bin"],
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"required": True,
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},
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"n_batch": {"display_name": "N Batch", "advanced": True},
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"n_ctx": {"display_name": "N Ctx", "advanced": True},
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"n_gpu_layers": {"display_name": "N GPU Layers", "advanced": True},
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"n_parts": {"display_name": "N Parts", "advanced": True},
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"n_threads": {"display_name": "N Threads", "advanced": True},
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"repeat_penalty": {"display_name": "Repeat Penalty", "advanced": True},
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"rope_freq_base": {"display_name": "Rope Freq Base", "advanced": True},
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"rope_freq_scale": {"display_name": "Rope Freq Scale", "advanced": True},
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"seed": {"display_name": "Seed", "advanced": True},
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"stop": {"display_name": "Stop", "advanced": True},
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"streaming": {"display_name": "Streaming", "advanced": True},
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"suffix": {"display_name": "Suffix", "advanced": True},
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"tags": {"display_name": "Tags", "advanced": True},
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"temperature": {"display_name": "Temperature"},
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"top_k": {"display_name": "Top K", "advanced": True},
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"top_p": {"display_name": "Top P", "advanced": True},
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"use_mlock": {"display_name": "Use Mlock", "advanced": True},
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"use_mmap": {"display_name": "Use Mmap", "advanced": True},
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"verbose": {"display_name": "Verbose", "advanced": True},
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"vocab_only": {"display_name": "Vocab Only", "advanced": True},
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"inputs": {"display_name": "Input"},
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}
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def build(
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self,
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model_path: str,
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inputs:str,
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grammar: Optional[str] = None,
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cache: Optional[bool] = None,
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client: Optional[Any] = None,
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echo: Optional[bool] = False,
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f16_kv: bool = True,
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grammar_path: Optional[str] = None,
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last_n_tokens_size: Optional[int] = 64,
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logits_all: bool = False,
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logprobs: Optional[int] = None,
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lora_base: Optional[str] = None,
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lora_path: Optional[str] = None,
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max_tokens: Optional[int] = 256,
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metadata: Optional[Dict] = None,
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model_kwargs: Dict = {},
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n_batch: Optional[int] = 8,
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n_ctx: int = 512,
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n_gpu_layers: Optional[int] = 1,
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n_parts: int = -1,
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n_threads: Optional[int] = 1,
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repeat_penalty: Optional[float] = 1.1,
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rope_freq_base: float = 10000.0,
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rope_freq_scale: float = 1.0,
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seed: int = -1,
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stop: Optional[List[str]] = [],
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streaming: bool = True,
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suffix: Optional[str] = "",
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tags: Optional[List[str]] = [],
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temperature: Optional[float] = 0.8,
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top_k: Optional[int] = 40,
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top_p: Optional[float] = 0.95,
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use_mlock: bool = False,
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use_mmap: Optional[bool] = True,
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verbose: bool = True,
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vocab_only: bool = False,
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) -> Text:
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output = LlamaCpp(
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model_path=model_path,
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grammar=grammar,
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cache=cache,
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client=client,
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echo=echo,
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f16_kv=f16_kv,
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grammar_path=grammar_path,
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last_n_tokens_size=last_n_tokens_size,
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logits_all=logits_all,
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logprobs=logprobs,
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lora_base=lora_base,
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lora_path=lora_path,
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max_tokens=max_tokens,
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metadata=metadata,
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model_kwargs=model_kwargs,
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n_batch=n_batch,
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n_ctx=n_ctx,
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n_gpu_layers=n_gpu_layers,
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n_parts=n_parts,
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n_threads=n_threads,
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repeat_penalty=repeat_penalty,
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rope_freq_base=rope_freq_base,
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rope_freq_scale=rope_freq_scale,
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seed=seed,
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stop=stop,
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streaming=streaming,
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suffix=suffix,
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tags=tags,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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use_mlock=use_mlock,
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use_mmap=use_mmap,
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verbose=verbose,
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vocab_only=vocab_only,
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)
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message = output.invoke(inputs)
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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@ -13,7 +13,7 @@ from langflow.field_typing import Text
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class ChatOllamaComponent(CustomComponent):
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display_name = "ChatOllama model"
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description = "Local LLM for chat with Ollama."
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description = "Generate text using Local LLM for chat with Ollama."
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def build_config(self) -> dict:
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return {
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@ -8,7 +8,7 @@ from langflow.field_typing import Text
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class ChatVertexAIComponent(CustomComponent):
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display_name = "ChatVertexAI model"
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description = "`Vertex AI` Chat large language models API."
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description = "Generate text using Vertex AI Chat large language models API."
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def build_config(self):
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return {
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