Instructions to use chkrishna2001/psm-memory-qwen-1.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="chkrishna2001/psm-memory-qwen-1.5b-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chkrishna2001/psm-memory-qwen-1.5b-gguf", dtype="auto")

llama-cpp-python

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="chkrishna2001/psm-memory-qwen-1.5b-gguf",
	filename="gguf/psm-memory-qwen-1.5b-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

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 chkrishna2001/psm-memory-qwen-1.5b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

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 chkrishna2001/psm-memory-qwen-1.5b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

Use Docker

docker model run hf.co/chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

LM Studio
Jan

vLLM

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chkrishna2001/psm-memory-qwen-1.5b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chkrishna2001/psm-memory-qwen-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

SGLang

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf 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 "chkrishna2001/psm-memory-qwen-1.5b-gguf" \
    --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": "chkrishna2001/psm-memory-qwen-1.5b-gguf",
		"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 "chkrishna2001/psm-memory-qwen-1.5b-gguf" \
        --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": "chkrishna2001/psm-memory-qwen-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with Ollama:
```
ollama run hf.co/chkrishna2001/psm-memory-qwen-1.5b-gguf:F16
```

Unsloth Studio

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf 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 chkrishna2001/psm-memory-qwen-1.5b-gguf 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 chkrishna2001/psm-memory-qwen-1.5b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chkrishna2001/psm-memory-qwen-1.5b-gguf to start chatting

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

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": "chkrishna2001/psm-memory-qwen-1.5b-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

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 chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

Run Hermes

hermes

Docker Model Runner
How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with Docker Model Runner:
```
docker model run hf.co/chkrishna2001/psm-memory-qwen-1.5b-gguf:F16
```

Lemonade

How to use chkrishna2001/psm-memory-qwen-1.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chkrishna2001/psm-memory-qwen-1.5b-gguf:F16

Run and chat with the model

lemonade run user.psm-memory-qwen-1.5b-gguf-F16

List all available models

lemonade list

PSM Memory Qwen 1.5B

PSM Memory is a small model trained for memory-management operations in agent systems. It is not intended to answer user questions directly. It is intended to decide what should be remembered, retrieve and rank relevant memories, detect conflicts, and support memory lifecycle operations.

This repository contains the runtime artifacts for the PSM Memory model derived from Qwen1.5-1.8B.

Files

GGUF runtime artifacts

gguf/psm-memory-qwen-1.5b-q4_k_m.gguf
- Recommended default runtime artifact for @psm-memory/cli and @psm-memory/pi-plugin.
- Quantized GGUF for local inference.
gguf/psm-memory-qwen-1.5b-f16.gguf
- Full precision GGUF export.
- Larger and slower, useful for quality comparison and future quantization.

Hugging Face / Transformers artifacts

merged-fp16/
- Merged FP16 model weights and tokenizer/config files.
- Useful for inspection, conversion, or further export.
lora/
- LoRA adapter weights and tokenizer/config files.
- Useful for reproducibility and future fine-tuning work.

Integrity

checksums.sha256
- SHA-256 checksums for uploaded artifacts.

Intended Use

Use this model as a memory-management component for an LLM agent:

User prompt arrives.
PSM Memory retrieves/ranks relevant memories and produces context for the main LLM.
Main LLM responds or takes an action.
PSM Memory evaluates the response/action and stores durable memory when appropriate.

The npm packages are:

@psm-memory/sdk
@psm-memory/cli
@psm-memory/pi-plugin

The CLI/plugin should use the Q4_K_M GGUF artifact by default.

Not Intended For

General chat completion.
Factual QA as a standalone assistant.
Safety-critical autonomous decision-making without external validation.
Storing private user data without consent and appropriate access controls.

License

This model is a fine-tuned/converted PSM Memory model derived from Qwen1.5-1.8B.

The model weights are distributed under the Tongyi Qianwen Research License inherited from the base model:

https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE

The PSM Memory npm package code is licensed separately in its source repository.

Training Data

The upload bundle does not include training data by default. If synthetic training data is published, it should be uploaded only after checking that it contains no private, personal, or proprietary content.

Recommended approach:

Add a separate training-data/ folder for synthetic examples.
Include a short data card explaining how the data was generated.
Include only sanitized synthetic data or a representative sample if the full dataset is large.

Reproducibility Notes

A Colab notebook is not required for using this model. It is useful if you want others to reproduce conversion, quantization, or adapter loading.

Recommended optional notebooks:

notebooks/convert-to-gguf.ipynb
notebooks/quantize-gguf.ipynb
notebooks/run-psm-memory.ipynb

These can be added later without blocking CLI setup support.

Downloads last month: 278

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

Model tree for chkrishna2001/psm-memory-qwen-1.5b-gguf

Base model

Qwen/Qwen1.5-1.8B

Adapter

(30582)

this model