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Language Model Deployment and Fine-tuning

21 posts · Aug 2022 – Apr 2026

What this trail covers

Replicate continues to expand its support for large language models, now including fine-tuning capabilities for Llama 2 and new models like StableLM, Vicuna, and GPT4All. The platform provides tools for creating custom models, authenticating API access, and initiating training jobs, with the ability to monitor progress and run the fine-tuned models via API or web interface. Playgrounds like OpenPlayground and AI Playground are emerging to compare and tune these models. This post introduces the Llama 3.1 405b model and details how to fine-tune FLUX.1 for personalized image generation, enabling users to create images of themselves in various scenarios using a simple web interface or API. The platform also highlights the ease of use for fine-tuning faces compared to previous models and provides guidance on prompt engineering with LLMs.

All Replicate trails
  1. How to make remarkable videos with Seedance 2.0This post introduces Seedance 2.0, a new video generation model on Replicate. It details Seedance 2.0's capabilities, including its ability to reference multiple input types (images, video, audio, text) for greater control over video generation. It highlights features like character consistency, motion transfer, style and composition control, and audio-driven rhythm. The post also emphasizes the model's unified architecture for synchronized audio and video, its handling of physics for realistic motion, and its multi-shot output with camera planning. Examples showcase its use for creating complex scenes, character animations, and physics-based simulations.
  2. Run Isaac 0.1 on ReplicateThis post introduces Isaac 0.1, a 2B-parameter, open-weight vision-language model. It highlights Isaac's capabilities in grounded visual reasoning (returning bounding boxes tied to claims), strong OCR in real-world conditions, and spatial awareness for physical environments. The model's efficiency is emphasized for real-time or edge-constrained applications. It also notes Isaac's ability to learn new tasks from examples without fine-tuning. A JavaScript API example is provided.
  3. IBM's Granite 4.0 is now on ReplicateThis post announces the availability of IBM's Granite 4.0 family of open-source small language models on Replicate. It details the hybrid architecture (Mamba-2 + Transformers) and MoE routing strategy that makes Granite 4.0 performant and efficient for long contexts and on consumer GPUs. Practical use cases like document summarization, RAG, and AI agents are highlighted. The post also emphasizes the open-source nature (Apache 2.0 license) and provides API integration examples for cURL, JavaScript, and Python.
  4. How to prompt Veo 3 with imagesThis post details the integration of image input capabilities into the Veo 3 model on Replicate. It explains how to leverage image inputs for style preservation, maintaining artistic integrity from cartoons to photographs. The post also covers Veo 3's proficiency in handling typography within animations and demonstrates how image inputs provide greater creative control over video generation by allowing users to pre-define aesthetics before animating. Specific examples showcase animating parts of an image, generating hyperrealistic footage, and creative prompting techniques.
  5. Run 30,000+ LoRAs on Hugging Face with ReplicateThis post details a new integration where Hugging Face's inference client can route requests to Replicate's backend models, specifically using the `black-forest-labs/flux-dev-lora` model. This allows for dynamic loading of LoRA weights from Hugging Face directly into Replicate's inference infrastructure. The mechanism involves passing the LoRA weights as a `lora_weights` parameter to the Replicate API, enabling the execution of over 30,000 LoRAs without needing a separate hosted model for each. This significantly expands the accessibility and usability of LoRAs for image generation.
  7. AI video is having its Stable Diffusion momentThis post details the emergence of advanced AI video generation models, highlighting several specific models available on Replicate. It provides a comparative analysis of these models based on ELO score, speed, duration, resolution, and open-source availability. It also mentions the fine-tuning capabilities for models like Genmo Mochi 1 and the ongoing development of new models and features.
  8. FLUX fine-tunes are now fastThis post details optimizations for running FLUX fine-tunes on Replicate, making them as fast as the base models. Key improvements include quantizing LoRAs to fp8, merging weights into the base model, and automatically increasing `lora_scale` by 1.5x when `go_fast=true` for better output quality. The post also introduces the ability to load LoRAs from Hugging Face, Civitai, and arbitrary HTTP URLs, and open-sources the improvements made to FLUX models.
  9. Fine-tune FLUX.1 with an APIThis post introduces the ability to programmatically fine-tune FLUX.1 image generation models using Replicate's HTTP API. It details the prerequisites, gathering and zipping training images, setting the API token, creating a destination model via API, uploading training data using the Files API, and initiating the training process with specific inputs like input_images and trigger_word. It also explains how to check training status, retrieve LoRA weights, and generate images using the fine-tuned model via the web playground and API.
  10. Fine-tune FLUX.1 to create images of yourselfThis post details the process of fine-tuning the FLUX.1 image generation model on Replicate using personal images. It outlines the prerequisites, image gathering and preparation (zipping), choosing a unique trigger word, and the steps for creating and training a model via the web-based training form. It also covers generating images using the web playground and API, and provides an example of using a language model (Meta Llama 3.1 405b) to generate better prompts for image generation. The post emphasizes the ease of fine-tuning faces with FLUX.1 compared to older models like Stable Diffusion.
  11. Run Meta Llama 3.1 405B with an APIThis post details the integration and API access for Meta's Llama 3.1 405B model on Replicate. It provides code examples for running the model via JavaScript, Python, and cURL, demonstrating how to use the `replicate.stream` function for real-time output. It also outlines the model's technical specifications (405 billion parameters, 8000 token context window, multilingual support) and links to relevant resources for responsible AI development and a demo chat app.
  12. Replicate Intelligence #7This post contributes to the language model deployment and fine-tuning thread by highlighting new models and tools, and by discussing advancements in data curation, synthetic data generation, custom classifier creation, and function-calling dataset generation. It also mentions new text-to-image models and novel ways to embed LLMs.
  13. Run Snowflake Arctic with an APIThis post details how to run the Snowflake Arctic language model via the Replicate API. It provides code examples for JavaScript, Python, and cURL, demonstrating how to integrate Arctic into applications. It also highlights the API playground for interactive testing and links to relevant documentation and client libraries.
  14. Run Meta Llama 3 with an APIThis post introduces the integration and API access for Meta's Llama 3 models (8B and 70B parameter, base and instruct-tuned variants) on the Replicate platform. It provides concrete code examples for running these models using JavaScript, Python, and cURL, demonstrating API calls, input parameters, and streaming output. It also details the different Llama 3 variants available and links to relevant documentation and a demo application.
  15. Run Code Llama 70B with an APIThis post introduces the availability and API usage of Code Llama 70B variants on Replicate. It provides code examples for running the model using JavaScript, Python, and cURL, demonstrating how to interact with the API for code generation tasks. It also links to the specific model pages and relevant client library documentation.
  16. How to create an AI narrator for your lifeThis post details the integration of multimodal AI models (Llava 13B, GPT-4-Vision) for image understanding and text-to-speech models (XTTS-v2, ElevenLabs) for audio generation, alongside language models for scriptwriting, to create an AI narrator. It provides specific model recommendations, cost considerations, and code snippets for building such an application, demonstrating a new capability for combining vision, language, and speech AI.
  17. Jet-setting with Llama 2 + GrammarsThis post introduces and demonstrates the use of Llama 2 models with built-in grammar support for constrained text generation. It provides a practical example of using a JSON schema to extract structured flight information from an email, showcasing the effectiveness of grammars for ensuring syntactic perfection in LLM output. The post also links to relevant resources for further exploration of Llama 2 and grammar-based generation.
  18. How to run Mistral 7B with an APIThis post introduces the Mistral 7B and Mistral 7B Instruct models to the Replicate platform, detailing their performance advantages (recent training data, speed via grouped-query attention and sliding window attention) and coding capabilities. It provides concrete examples of API integration using JavaScript and Python client libraries, as well as direct cURL commands, enabling users to run these models with a single line of code. It also points to fine-tuned versions and comparison tools.
  19. Streaming output for language modelsIntroduced server-sent event streams for language models, allowing for real-time token-by-token output. Provided Node.js and cURL examples for consuming these streams. Documented how to add streaming support to custom Cog models by yielding tokens.
  20. Fine-tune Llama 2 on ReplicateThis post details the process of fine-tuning Llama 2 models on Replicate for specific tasks, using text summarization as an example. It outlines the supported Llama 2 models, the required training data format (JSONL), how to create a new model on Replicate, authenticate API access, and initiate a training job using the Replicate Python library. It also demonstrates how to run the fine-tuned model with a prompt and provides next steps for users.
  21. Language model roundup, April 2023This post announces new language models available on Replicate: StableLM, Vicuna, and GPT4All, in addition to existing models like FLAN-T5, GPT-J, and LLaMA. It also highlights new playgrounds for comparing and tuning LLMs, such as OpenPlayground and AI Playground, and discusses the emergence of autonomous agent projects like Auto-GPT, BabyAGI, and Teenage-AGI.
  22. Language models are on ReplicateThis post introduces the ability to deploy, run, and fine-tune large language models on Replicate. It highlights official versions of FLAN-T5, GPT-J, and LLaMA, and the capability to push custom models. A preview of fine-tuning capabilities is also released, allowing users to fine-tune Flan-T5, GPT-J, and LLaMA with provided code examples for running models and initiating training jobs.
  23. Fine-tune LLaMA to speak like Homer SimpsonThis post details the process of fine-tuning the LLaMA model to adopt the speaking style of a specific character, using a dataset of ~60K lines of dialogue from The Simpsons. It outlines the data preparation steps, including parsing dialogue into context-response pairs, and modifying the training script from the Alpaca codebase. The prompt engineering for character-specific responses is also described, along with instructions for cloning the repository, checking out the 'homerbot' branch, and running predictions.
  24. Train and run Stanford Alpaca on your own machineThis post details the process of training and running Stanford Alpaca on personal hardware. It covers obtaining LLaMA weights, converting them to a Hugging Face transformers-compatible format using a provided script, and then training the model using a `train_model.sh` script within a Cog environment. The post also demonstrates how to run the trained model using `cog predict` and provides instructions for pushing the model to Replicate for cloud deployment.
  25. Introducing LoRA: A faster way to fine-tune Stable DiffusionThis post introduces and integrates LoRA (Low-Rank Adaptation) as a new, faster method for fine-tuning Stable Diffusion models. It details the LoRA technique, its advantages over DreamBooth (faster training, smaller model size, faster generation, ability to combine concepts), and provides a step-by-step guide on how to gather training images, upload them, train LoRA models using `replicate/lora-training` and `replicate/lora-advanced-training`, and generate images using the `replicate/lora` prediction model. It highlights the immediate availability of LoRA predictions without cold boots.
  26. Run Stable Diffusion with an APIThis post details how to use the Replicate API to run the Stable Diffusion model. It covers installing the Python client, authenticating with an API token, and making predictions using the `replicate.models.get` and `version.predict` methods. It also mentions the ability to view past predictions and links to further examples and documentation.