Instructions to use sennaLLMLearner/qwen2.5-7b-memory-distiller with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with Transformers:

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

pipe = pipeline("text-generation", model="sennaLLMLearner/qwen2.5-7b-memory-distiller")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sennaLLMLearner/qwen2.5-7b-memory-distiller")
model = AutoModelForCausalLM.from_pretrained("sennaLLMLearner/qwen2.5-7b-memory-distiller")
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 sennaLLMLearner/qwen2.5-7b-memory-distiller with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sennaLLMLearner/qwen2.5-7b-memory-distiller",
	filename="qwen2.5-7b-memory-distiller.Q4_K_M.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 sennaLLMLearner/qwen2.5-7b-memory-distiller 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 sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sennaLLMLearner/qwen2.5-7b-memory-distiller: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 sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sennaLLMLearner/qwen2.5-7b-memory-distiller: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 sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

Use Docker

docker model run hf.co/sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

LM Studio
Jan

vLLM

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sennaLLMLearner/qwen2.5-7b-memory-distiller"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sennaLLMLearner/qwen2.5-7b-memory-distiller",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

SGLang

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller 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 "sennaLLMLearner/qwen2.5-7b-memory-distiller" \
    --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": "sennaLLMLearner/qwen2.5-7b-memory-distiller",
		"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 "sennaLLMLearner/qwen2.5-7b-memory-distiller" \
        --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": "sennaLLMLearner/qwen2.5-7b-memory-distiller",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with Ollama:
```
ollama run hf.co/sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M
```

Unsloth Studio

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller 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 sennaLLMLearner/qwen2.5-7b-memory-distiller 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 sennaLLMLearner/qwen2.5-7b-memory-distiller to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sennaLLMLearner/qwen2.5-7b-memory-distiller to start chatting

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sennaLLMLearner/qwen2.5-7b-memory-distiller: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": "sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sennaLLMLearner/qwen2.5-7b-memory-distiller: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 sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with Docker Model Runner:
```
docker model run hf.co/sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M
```

Lemonade

How to use sennaLLMLearner/qwen2.5-7b-memory-distiller with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sennaLLMLearner/qwen2.5-7b-memory-distiller:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-7b-memory-distiller-Q4_K_M

List all available models

lemonade list

Conversation Memory Distiller (Qwen2.5-7B)

A small model fine-tuned to turn a conversation log into clean, searchable structured-memory JSON for later recall, running locally on an edge device. Fine-tuned from Qwen/Qwen2.5-7B-Instruct via SFT warmup → ORPO, distilling a DeepSeek-V3 teacher, and optimized to follow 5 content principles (below) when compressing a conversation into memory.

This is a structured-distillation (memory-summarization) model, not a general summarizer. It is meant to run locally on an edge device and emit schema-conformant memory objects.

Why this exists

This model powers the local memory layer of codeatrium, a personal-memory app: it distills conversations into searchable memories. The goal was a local / free / private 7B that runs on an edge device (via Ollama Q4_K_M) and could replace the larger distiller previously used in that role. It's a personal project: the dataset and evaluation are small-scale, the "5 principles" are a custom rubric drawn from the memory-distillation literature, and training was stopped pragmatically once it reached practical quality (2 ORPO epochs), not pushed to convergence. Metrics and acceptance gates were pre-registered before running the final evals, and only what survived is reported (see How I know).

Intended use / Out of scope

Intended: distilling a conversation (user+assistant turns) into a structured memory object (JSON/record with fields) for later search / recall, running locally.
Out of scope: general-purpose summarization; downstream retrieval/QA quality (not evaluated here — only distillation quality is); absolute faithfulness guarantees.

Learning signal: the 5 core principles

The model is trained with these 5 principles as the preference signal (chosen/rejected). Each is a content-level best practice backed by external work on memory systems and retrieval.

Principle	Definition	Primary source	Basis (key figure)	How it's measured
completeness (recall)	capture the source's important specifics without dropping them	ProMem (arXiv:2601.04463)	verification+completion raises memory integrity 54→74%; +30.89pp vs Mem0 on HaluMem; +10.74pt transfer on Llama3-8B (the one principle confirmed to help at small scale)	deterministic: specifics recall against the source, no LLM judge
lexical_fidelity	keep numbers, commands, identifiers verbatim, not paraphrased	SPAR (arXiv:2110.06918) / Searchat (arXiv:2603.13017)	dense retrievers miss rare entities / salient phrases that BM25 catches (lexical-fidelity gap); Searchat retains 96.8% of query vocabulary after distillation	deterministic: verbatim-retention rate among captured specifics (correlated with completeness, not fully independent)
atomicity	one memory = one fact; no cramming multiple facts together	TriMem (arXiv:2605.19952) / Mem0	fact-centric atomic decomposition; coarse-only memory blocks deep reasoning across scattered facts	LLM judge only; no deterministic anchor
discriminative	carries specific info that can be retrieved / told apart (not generic labels)	GAAMA (arXiv:2603.27910) / HippoRAG (arXiv:2405.14831)	generic labels ("programming") make megahub nodes that absorb retrieval mass (megahub problem); discriminative tags narrow retrieval	LLM judge only; no deterministic anchor
faithfulness (precision)	do not fabricate specifics absent from the source	ProMem (arXiv:2601.04463) / HaluMem (arXiv:2511.03506)	stronger completion increases fabrication risk (the flip side of completeness); HaluMem measures hallucination	deterministic: specifics precision against the source, no LLM judge

Eval note. completeness and faithfulness are measured without an LLM judge as set recall / precision of specifics (numbers, identifiers, commands, paths, quoted strings) against the source. lexical_fidelity is a supplementary verbatim-retention rate (not independent of completeness). atomicity and discriminative require structural/semantic judgment and are measured only via LLM judges — they carry no deterministic anchor (see limitations).

Training

Base: Qwen/Qwen2.5-7B-Instruct. Teacher: DeepSeek-V3 (distillation target).
Data: technical conversations deterministically sampled from WildChat-1M (en+ja, toxic-filtered) — a small preference set (~hundreds of conversations / thousands of exchanges).
Recipe: SFT warmup → merge → fresh LoRA (r16) → ORPO (2 epochs).

How I know

I evaluated distillation quality (the 5 principles), not downstream retrieval. Metrics and acceptance gates were pre-registered before the final runs.

It beats the incumbent on the deployment task. On my codeatrium production path (all arms Ollama Q4_K_M, 48 independent real conversations), this 7B outperforms both qwen2.5-coder:14b (my previous distiller) and raw qwen2.5:7b. The win is not judge-dependent — it holds under a same-family judge (Qwen2.5-72B), a different-family judge (Llama-3.3-70B), a judge-free specifics metric, and a blind human rating (n=30, FT vs the 14B):

completeness is confirmed by all four methods (human net +0.63, p≈7e-5) — the flagship result.
discriminative (+0.47) and lexical_fidelity (+0.37) are also human-confirmed vs the 14B.

It generalizes to unseen structured schemas — but not to everything. On 5 held-out schemas it never trained on (measured judge-free), it beats raw 7B on field-structured formats (nested JSON, typed record, entity-relation graph — all significant) but only ties on flat / hard-length-capped formats (flat Markdown bullets, ≤12-word YAML). So the honest claim is: generalizes to unseen field-structured schemas; no edge on flat / heavily-constrained formats. (codeatrium's schema is field-structured, consistent with the deployment win.)

Limitations (read this)

Small-scale & personal. Small dataset; evals of n=30–48; training stopped at practical utility.
atomicity is measurement-limited. LLM judges score a large FT win, but blind humans rated it a near-tie (27/30 ties) — the judge win is not human-confirmed. Treat atomicity cautiously.
discriminative is family-sensitive. Human- and judge-confirmed vs the 14B, but vs raw 7B it was not significant under a cross-family judge.
The human eval is author-blind, not third-party. I rated blind (arm-anonymized, order randomized), which removes judge-family bias, but I built the model and know its style — so it is not a fully independent human evaluation. (It was conservative: 88/150 ratings were ties.)
The 5 principles are my own rubric, not a standard benchmark; absolute faithfulness is low on hard prompts (the claim is "more faithful than the baselines," paired — not "faithful" in absolute terms).
Downstream utility (retrieval/QA) is not evaluated. Scope is distillation quality only.

Usage

Deployment target is Ollama Q4_K_M, called via an OpenAI-compatible endpoint (http://localhost:11434/v1) — the same path codeatrium uses. Prompt the model with your distillation instruction + schema + the conversation, and read back the structured JSON (greedy / temperature 0 for determinism). The 5-principle behavior is strongest on field-structured schemas.

Technical note: this repo ships the fully merged weights (base ⊕ SFT-LoRA ⊕ ORPO-LoRA already baked in) — load it directly like any Qwen2.5-7B checkpoint, no adapter reconstruction needed. Internally it was trained as stock Qwen ⊕ SFT-LoRA → merge → ⊕ ORPO-LoRA → merge; that provenance only matters if you're retraining from an intermediate checkpoint, not for inference.

References

ProMem — proactive memory verification & completion. arXiv:2601.04463
SPAR — lexical-fidelity gap of dense retrievers. arXiv:2110.06918 (EMNLP-Findings 2022)
Searchat — structured distillation / surviving vocabulary. arXiv:2603.13017
TriMem — multi-granularity memory (segment / atomic fact / profile). arXiv:2605.19952
HaluMem — memory hallucination benchmark. arXiv:2511.03506
GAAMA — graph-augmented associative memory (megahub avoidance). arXiv:2603.27910 / github.com/swarna-kpaul/gaama
HippoRAG — neurobiologically-inspired long-term memory / entity-centric KG retrieval (hub-concentration evidence). arXiv:2405.14831