Reinforcement Learning
Transformers
GGUF
Chinese
English
incremental-pretraining
sft
roleplay
cot
conversational
Instructions to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4", dtype="auto") - llama-cpp-python
How to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4", filename="Tifa-DeepsexV2-7b-0218-Q4_KM.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 ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 # Run inference directly in the terminal: llama-cli -hf ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 # Run inference directly in the terminal: llama-cli -hf ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
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 ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 # Run inference directly in the terminal: ./llama-cli -hf ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
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 ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
Use Docker
docker model run hf.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
- LM Studio
- Jan
- Ollama
How to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with Ollama:
ollama run hf.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
- Unsloth Studio
How to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 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 ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 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 ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with Docker Model Runner:
docker model run hf.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
- Lemonade
How to use ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
Run and chat with the model
lemonade run user.Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4-{{QUANT_TAG}}List all available models
lemonade list
File size: 1,533 Bytes
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TEMPLATE """
{{ if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{ .System }}
{{- if .Tools }}
# Tools
You are provided with function signatures within <tools></tools> XML tags:
<tools>{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}{{- 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>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>tool
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>" |