Instructions to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2", filename="Ornith-Agents-A1-3.6-35B-A3B-dare_ties-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2: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 tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2: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 tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M
Use Docker
docker model run hf.co/tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 with Ollama:
ollama run hf.co/tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M
- Unsloth Studio
How to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 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 tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 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 tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 with Docker Model Runner:
docker model run hf.co/tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M
- Lemonade
How to use tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tepirale/Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2:Q4_K_M
Run and chat with the model
lemonade run user.Ornith-Agents-A1-3.6-35B-A3B-GGUF-v2-Q4_K_M
List all available models
lemonade list
Se hizo una extraccion de MTP de un modelo 35b, formateo pesos.
experimento 20
creo que elimine el template de los modelos
el injerto MTP del otro modelo me gusto con este modelo , me falta recrear este modelo para agregar el template
en algun momento lo borrare
wget -O /content/chat_template.jinja tepirale/Ornith-Agents-A1-3.6-35B-A3B-dare_ties
Uso (llama.cpp con MTP)
llama-server -m Ornith-Agents-A1-3.6-35B-A3B-dare_ties-Q4_K_M.gguf \
--spec-type draft-mtp --spec-draft-n-max 2 \
-ngl 99 -fa on -c 65536 --jinja \
--cache-type-k q8_0 --cache-type-v q8_0
/content/llama.cpp/build-cuda/bin/llama-server \
-m /content/work/gguf/Ornith-Agents-A1-3.6-35B-A3B-dare_ties-Q4_K_M.gguf \
--chat-template-file /content/chat_template.jinja \
--spec-type draft-mtp \
--spec-draft-n-max 3 \
--spec-draft-n-min 1 \
--host 0.0.0.0 --port 8080 \
--n-gpu-layers 999 \
--ctx-size 20000 \
--flash-attn on \
--cont-batching \
--parallel 4 \
--cache-type-k q8_0 \
--cache-type-v q8_0 \
--metrics
```bash
## Quants
| Quant | Tamaño |
|-------|--------|
| `Q4_K_M` | 20.5 GiB |
| `Q5_K_M` | 23.9 GiB |
| `Q6_K` | 27.4 GiB |
| `Q8_0` | 35.2 GiB |
## GPU 3090 24GB | uso 22GB libre ~2GB | modelo Q4_K_M.gguf
max_tok | compl_tok | time(s) | tok/s medido | tok/s nativo | draft acc% | finish
---------------------------------------------------------------------------------------
512 | 512 | 3.11 | 164.64 | 185.81154644562835 | 58.6% | length
1024 | 1024 | 6.38 | 160.6 | 168.1821675659376 | 49.6% | length
1536 | 1536 | 9.23 | 166.42 | 174.37983333556605 | 52.5% | length
2048 | 2048 | 11.82 | 173.27 | 180.66270396669316 | 56.3% | length
2560 | 2560 | 15.35 | 166.77 | 173.19459183649997 | 52.7% | length
3072 | 3072 | 18.23 | 168.52 | 174.07312295115594 | 53.1% | length
3584 | 3584 | 21.77 | 164.61 | 170.3211904082557 | 51.6% | length
4096 | 4096 | 24.66 | 166.11 | 171.41060296776368 | 52.3% | length
4608 | 4608 | 26.66 | 172.83 | 178.31869156802577 | 56.8% | length
5120 | 5120 | 30.05 | 170.39 | 175.3896167291549 | 54.9% | length
5632 | 5632 | 34.0 | 165.65 | 170.00963609304574 | 52.2% | length
6144 | 6144 | 37.16 | 165.34 | 170.1135424832823 | 52.4% | length
6656 | 6656 | 40.8 | 163.14 | 171.59782245044528 | 53.3% | length
7168 | 7168 | 47.57 | 150.68 | 153.01051061387676 | 54.1% | length
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