Instructions to use jacob-ml/jacob-24b-prod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jacob-ml/jacob-24b-prod with PEFT:
Task type is invalid.
- Transformers
How to use jacob-ml/jacob-24b-prod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jacob-ml/jacob-24b-prod") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jacob-ml/jacob-24b-prod", dtype="auto") - llama-cpp-python
How to use jacob-ml/jacob-24b-prod with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jacob-ml/jacob-24b-prod", filename="jacob-24b-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use jacob-ml/jacob-24b-prod with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: llama-cli -hf jacob-ml/jacob-24b-prod:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: llama-cli -hf jacob-ml/jacob-24b-prod:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf jacob-ml/jacob-24b-prod:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf jacob-ml/jacob-24b-prod:Q4_K_M
Use Docker
docker model run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use jacob-ml/jacob-24b-prod with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jacob-ml/jacob-24b-prod" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jacob-ml/jacob-24b-prod", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- SGLang
How to use jacob-ml/jacob-24b-prod with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "jacob-ml/jacob-24b-prod" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jacob-ml/jacob-24b-prod", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "jacob-ml/jacob-24b-prod" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jacob-ml/jacob-24b-prod", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use jacob-ml/jacob-24b-prod with Ollama:
ollama run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- Unsloth Studio new
How to use jacob-ml/jacob-24b-prod with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jacob-ml/jacob-24b-prod to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jacob-ml/jacob-24b-prod to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jacob-ml/jacob-24b-prod to start chatting
- Pi new
How to use jacob-ml/jacob-24b-prod with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "jacob-ml/jacob-24b-prod:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jacob-ml/jacob-24b-prod with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default jacob-ml/jacob-24b-prod:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use jacob-ml/jacob-24b-prod with Docker Model Runner:
docker model run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- Lemonade
How to use jacob-ml/jacob-24b-prod with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jacob-ml/jacob-24b-prod:Q4_K_M
Run and chat with the model
lemonade run user.jacob-24b-prod-Q4_K_M
List all available models
lemonade list
Question about vLLM
Hi!
I saw a while ago you posted this question when trying to run the fine-tuned mistral-small-3.2 in vLLM:
https://discuss.vllm.ai/t/mistral-small-3-2-finetune-errors-out-there-is-no-module-or-parameter-named-language-model-in-llamaforcausallm/1764
I was wondering if you eventually found a fix for it? I am running into the same exact issue.
Thanks
Hey there, and thanks for reaching out.
The simple answer is that, due to this bug, I stopped using vLLM. I did not get it working and nobody was able to help, unfortunately.
I since switched to llama.cpp because it does what it's supposed to do. I am sure that there is a simple fix for vLLM, and that using vLLM would've probably been better, but it's weird that running such a popular language model on such a popular inference engine is so stupidly hard, so I didn't want to bother.
Switching to llama.cpp meant a lot of extra work for me. I since started maintaining a serverless endpoint repository for the engine on the RunPod hub (so if you're using RunPod, check https://console.runpod.io/hub/Jacob-ML/inference-worker). Community support and adoption of llama.cpp is bigger because it is a way more popular inference engine anyway, thus getting help is much easier.
Please keep me updated if you find a fix! That would be of great help.
Good luck :) would be so cool if anyone from vLLM could help with that issue...
Thanks for getting back to me!
That's unfortunate that you didn't find a fix; I was asking around in a few Discord servers. I think the issue is that the vLLM implementation for Mistral models expects them all to be in the custom Mistral config/weights/tokenizer that the official models are uploaded as. Fine-tuned models look like the more standard HuggingFace/Transformers format which I think trips up vLLM's logic.
Anyways, I'll let you know if I figure something out. In the meantime, I guess llama.cpp is an option...