The request names a specific fine-tuned model, Llama-3.1-8B-Instruct-sft, which is not itself a listed product; the closest matches are hosts where Llama 3.1 8B Instruct models can be run, fine-tuned, or served. Hugging Face hosts the model family and offers fine-tuning tooling 3, Replicate runs open-source models through a cloud API and lists a Llama-3.1-8B-based safety model 2, and Ollama runs open models locally or in the cloud 7.
A question back: Do you want to run this model, fine-tune it, or serve it through an API, and on which platform or hardware?
- 1
Hugging Facehuggingface.coverified and signed
Hosts open models and datasets and provides tooling to train or fine-tune language models with TRL or Unsloth 3.
official:MCP server · @huggingface/mcp-clientSDK · huggingface-hubCLI · text-embeddings-inferencerepository · pwc-cliskill · hf-cli+23 community tools built for it
for agents: huggingface-llm-trainer · llm · huggingface-best
- 2
Replicatereplicate.comverified and signed
Runs open-source machine learning models through a cloud API, including Llama-family models such as an 8B instruct model 2.
+14 community tools built for it
for agents: meta/llama-guard-3-8b · meta/meta-llama-3-8b-instruct · ibm-granite/granite-3.2-8b-instruct
- 3
Ollamaollama.comwebsite read, tools not yet checked
Runs open AI models locally or in the cloud on macOS, Windows and Linux 7.
+141 community tools built for it
- 4
NVIDIAnvidia.comverified and signed
Its Brev.dev console lists instances and indexes a Llama3.1-8b-instruct task for running this model 1.
official:MCP server · NVIDIA CUDA Docs+84 community tools built for it
for agents: llama3.1-8b-instruct · llama3.1 · llama3.1-8b-instruct
Sources
- 1Brev.dev Consolebrev.nvidia.com · read 2026-10-29
- 2Replicate - Run AI with an APIreplicate.com · read 2026-10-29
- 3Hugging Face – The AI community building the future.huggingface.co · read 2026-10-29
- 7Ollama · Run AI models locally and in the cloudollama.com · read 2026-10-29
For agents: the 10 actions behind this answer
llama3.1-8b-instruct · Llama-3.1-8B-Instruct verified and signed
Task: Llama3.1-8b-instruct
meta/llama-guard-3-8b · meta/llama-guard-3-8b verified and signed
A Llama-3.1-8B pretrained model, fine-tuned for content safety classification
huggingface-llm-trainer · huggingface-llm-trainer verified and signed
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts
createFineTune · OpenAI API verified and signed
Creates a job that fine-tunes a specified model from a given dataset. Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete. [Learn m
aws-ai-ml · aws-ai-ml verified and signed
Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning t
prompt-optimizer · prompt-optimizer verified and signed
Turn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for any LLM model inside a chat interface — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for chat. Trigger phrases inc
· Ollama website read, tools not yet checked
Ollama is the easiest way to run and build with open AI models locally or in the cloud on macOS, Windows, and Linux.
workers-ai-search-model · Cloudflare API verified and signed
Model Search
langchain · Docs by LangChain verified and signed
Use when building AI agents, integrating language models with
LLM · llama-index-core verified and signed
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.