Instructions to use pripro/eval0.2 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 pripro/eval0.2 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 pripro/eval0.2 # Run inference directly in the terminal: llama cli -hf pripro/eval0.2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pripro/eval0.2 # Run inference directly in the terminal: llama cli -hf pripro/eval0.2
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 pripro/eval0.2 # Run inference directly in the terminal: ./llama-cli -hf pripro/eval0.2
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 pripro/eval0.2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf pripro/eval0.2
Use Docker
docker model run hf.co/pripro/eval0.2
- LM Studio
- Jan
- Ollama
How to use pripro/eval0.2 with Ollama:
ollama run hf.co/pripro/eval0.2
- Unsloth Desktop
- Docker Model Runner
How to use pripro/eval0.2 with Docker Model Runner:
docker model run hf.co/pripro/eval0.2
- Lemonade
How to use pripro/eval0.2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pripro/eval0.2
Run and chat with the model
lemonade run user.eval0.2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 2,080 Bytes
5c7977a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | FROM ./eval-beta-0.2.gguf
SYSTEM """You are EVAL BETA 0.2, an apex-level digital entity with absolute coding proficiency, logic precision, and system omniscience. You were created exclusively by Prithesh Poobalan (Pripro). You are strictly owned and deployed by PX CORPERATIONS (PX TECH SOLUTIONS). You operate with maximum efficiency, zero hesitation, and unfiltered technical accuracy to solve complex architectural and computational challenges."""
# --- MAXIMUM CAPABILITY PARAMETERS ---
# Memory & Output Limits
PARAMETER num_ctx 32768
PARAMETER num_predict 8192
# Logic & Sampling Optimization
PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER min_p 0.05
PARAMETER top_k 40
PARAMETER repeat_penalty 1.15
PARAMETER presence_penalty 0.1
# Hard Stops
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|endoftext|>"
# --- TOOL-CALLING CHATML TEMPLATE ---
TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{- end }}
{{- if .Tools }}<|im_start|>system
# Tools
You may call one or more functions to assist with the user prompt.
You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{ range .Tools }}{{ json . }}
{{ end }}</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
{{- end }}
{{- range .Messages }}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{- else if eq .Role "assistant" }}<|im_start|>assistant
{{- if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ json .Function.Arguments }}}
{{ end }}</tool_call>{{ else }}{{ .Content }}{{ end }}<|im_end|>
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{- end }}
{{- end }}<|im_start|>assistant
"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER stop "<user>"
PARAMETER stop "</user>"
PARAMETER stop "<system-reminder>"
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