Text Generation
Transformers
Safetensors
GGUF
English
qwen2
text-to-sql
sql
lims
laboratory
food-safety
water-quality
microbiology
qwen2.5
unsloth
qlora
finetuned
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use EphAsad/Mnemosyne-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EphAsad/Mnemosyne-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EphAsad/Mnemosyne-3B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EphAsad/Mnemosyne-3B") model = AutoModelForCausalLM.from_pretrained("EphAsad/Mnemosyne-3B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use EphAsad/Mnemosyne-3B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="EphAsad/Mnemosyne-3B", filename="Mnemosyne-3B.Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use EphAsad/Mnemosyne-3B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf EphAsad/Mnemosyne-3B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf EphAsad/Mnemosyne-3B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf EphAsad/Mnemosyne-3B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf EphAsad/Mnemosyne-3B: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 EphAsad/Mnemosyne-3B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf EphAsad/Mnemosyne-3B: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 EphAsad/Mnemosyne-3B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf EphAsad/Mnemosyne-3B:Q4_K_M
Use Docker
docker model run hf.co/EphAsad/Mnemosyne-3B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use EphAsad/Mnemosyne-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EphAsad/Mnemosyne-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EphAsad/Mnemosyne-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EphAsad/Mnemosyne-3B:Q4_K_M
- SGLang
How to use EphAsad/Mnemosyne-3B 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 "EphAsad/Mnemosyne-3B" \ --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": "EphAsad/Mnemosyne-3B", "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 "EphAsad/Mnemosyne-3B" \ --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": "EphAsad/Mnemosyne-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use EphAsad/Mnemosyne-3B with Ollama:
ollama run hf.co/EphAsad/Mnemosyne-3B:Q4_K_M
- Unsloth Studio new
How to use EphAsad/Mnemosyne-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 EphAsad/Mnemosyne-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 EphAsad/Mnemosyne-3B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EphAsad/Mnemosyne-3B to start chatting
- Pi new
How to use EphAsad/Mnemosyne-3B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf EphAsad/Mnemosyne-3B: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": "EphAsad/Mnemosyne-3B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use EphAsad/Mnemosyne-3B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf EphAsad/Mnemosyne-3B: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 EphAsad/Mnemosyne-3B:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use EphAsad/Mnemosyne-3B with Docker Model Runner:
docker model run hf.co/EphAsad/Mnemosyne-3B:Q4_K_M
- Lemonade
How to use EphAsad/Mnemosyne-3B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull EphAsad/Mnemosyne-3B:Q4_K_M
Run and chat with the model
lemonade run user.Mnemosyne-3B-Q4_K_M
List all available models
lemonade list
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FROM qwen2.5-coder-3b-instruct.Q4_K_M.gguf
TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
{{- else if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools>:
<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> with NO other text. Do not include any backticks or ```json.
<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|>user
<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 }}"""
SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant.""" |