Instructions to use simmo/llama3.2-pyfim-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use simmo/llama3.2-pyfim-3b 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 simmo/llama3.2-pyfim-3b:Q8_0 # Run inference directly in the terminal: llama cli -hf simmo/llama3.2-pyfim-3b:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf simmo/llama3.2-pyfim-3b:Q8_0 # Run inference directly in the terminal: llama cli -hf simmo/llama3.2-pyfim-3b:Q8_0
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 simmo/llama3.2-pyfim-3b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf simmo/llama3.2-pyfim-3b:Q8_0
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 simmo/llama3.2-pyfim-3b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf simmo/llama3.2-pyfim-3b:Q8_0
Use Docker
docker model run hf.co/simmo/llama3.2-pyfim-3b:Q8_0
- LM Studio
- Jan
- Ollama
How to use simmo/llama3.2-pyfim-3b with Ollama:
ollama run hf.co/simmo/llama3.2-pyfim-3b:Q8_0
- Unsloth Studio
How to use simmo/llama3.2-pyfim-3b 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 simmo/llama3.2-pyfim-3b 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 simmo/llama3.2-pyfim-3b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for simmo/llama3.2-pyfim-3b to start chatting
- Docker Model Runner
How to use simmo/llama3.2-pyfim-3b with Docker Model Runner:
docker model run hf.co/simmo/llama3.2-pyfim-3b:Q8_0
- Lemonade
How to use simmo/llama3.2-pyfim-3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull simmo/llama3.2-pyfim-3b:Q8_0
Run and chat with the model
lemonade run user.llama3.2-pyfim-3b-Q8_0
List all available models
lemonade list
- Atomic Chat
Upload tokenizer
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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}
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},
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"bos_token": "<|begin_of_text|>",
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"chat_template": "{{- if .Suffix }}<|fim_begin|>{{ .Prompt }}<|fim_hole|>{{ .Suffix }}<|fim_end|>\n{
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"model_input_names": [
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}
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},
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"bos_token": "<|begin_of_text|>",
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"chat_template": "{{- if .Suffix }}<|fim_begin|>{{ .Prompt }}<|fim_hole|>{{ .Suffix }}<|fim_end|>\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\n\n\" }}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n{%- for message in messages %}\n{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}\n{%- endfor %}\n{%- if add_generation_prompt %}\n{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}\n{%- endif %}\n",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"model_input_names": [
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