Instructions to use Berk/reasongraph-extractor-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Berk/reasongraph-extractor-1.7b with Transformers:

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

pipe = pipeline("text-generation", model="Berk/reasongraph-extractor-1.7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Berk/reasongraph-extractor-1.7b")
model = AutoModelForCausalLM.from_pretrained("Berk/reasongraph-extractor-1.7b", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Berk/reasongraph-extractor-1.7b 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 Berk/reasongraph-extractor-1.7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Berk/reasongraph-extractor-1.7b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Berk/reasongraph-extractor-1.7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Berk/reasongraph-extractor-1.7b: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 Berk/reasongraph-extractor-1.7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Berk/reasongraph-extractor-1.7b: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 Berk/reasongraph-extractor-1.7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Berk/reasongraph-extractor-1.7b:Q4_K_M

Use Docker

docker model run hf.co/Berk/reasongraph-extractor-1.7b:Q4_K_M

LM Studio
Jan

vLLM

How to use Berk/reasongraph-extractor-1.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Berk/reasongraph-extractor-1.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Berk/reasongraph-extractor-1.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Berk/reasongraph-extractor-1.7b:Q4_K_M

SGLang

How to use Berk/reasongraph-extractor-1.7b 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 "Berk/reasongraph-extractor-1.7b" \
    --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": "Berk/reasongraph-extractor-1.7b",
		"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 "Berk/reasongraph-extractor-1.7b" \
        --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": "Berk/reasongraph-extractor-1.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Berk/reasongraph-extractor-1.7b with Ollama:
```
ollama run hf.co/Berk/reasongraph-extractor-1.7b:Q4_K_M
```
Unsloth Desktop

How to use Berk/reasongraph-extractor-1.7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Berk/reasongraph-extractor-1.7b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Berk/reasongraph-extractor-1.7b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Berk/reasongraph-extractor-1.7b with Docker Model Runner:
```
docker model run hf.co/Berk/reasongraph-extractor-1.7b:Q4_K_M
```

Lemonade

How to use Berk/reasongraph-extractor-1.7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Berk/reasongraph-extractor-1.7b:Q4_K_M

Run and chat with the model

lemonade run user.reasongraph-extractor-1.7b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Berk/reasongraph-extractor-1.7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Berk/reasongraph-extractor-1.7b: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 Berk/reasongraph-extractor-1.7b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Berk/reasongraph-extractor-1.7b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Berk/reasongraph-extractor-1.7b: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 "Berk/reasongraph-extractor-1.7b: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"

reasongraph-extractor-qwen3-1.7b

One small LLM for the three reasongraph extraction tasks -- causal cause/effect/signal spans, contradiction detection, and entity extraction -- from a single LoRA adapter over Qwen/Qwen3-1.7B, selected by a task tag and returning strict JSON. This is the conflict-capable, llama.cpp-servable variant: unlike the qwen3.5 extractor, this base runs in llama.cpp today, so it is the model behind reasongraph's self-hosted FineTunedConflictResolver.

Method: LoRA (r=16, alpha=32, dropout 0.05, all linear layers, 3 epochs, completion-only loss), merged to fp16.

Tasks & prompt format

Prompt with a bare task tag (no chat template) and greedily decode the JSON completion.

Tag	Input	Output
`[causal]`	`[causal] <sentence>`	`{"causal": true
`[conflict]`	`[conflict] existing: <fact A>\nnew: <fact B>`	`{"conflict": true
`[entities]`	`[entities] <sentence>`	`{"entities": ["...", ...]}`

Evaluation (L1)

Metric	value
CNC subtask-2 dev F1 (official scorer)	0.666
Conflict F1 (40 hand pairs, fp16)	0.952 (matches gpt-oss:20b; > K2 0.77, NLI 0.78)
Synthetic multilingual C / E / S (seqeval)	0.969 / 0.957 / 0.935
Gate precision (CNC-news / synth / prose)	0.77 / 0.99 / 1.0
E2 causal reasoning eval (Chain / Answer)	84 / 75
CPU Q4_K_M causal latency (4 threads)	~2238 ms/sentence, ~1609 sentences/hour, ~2.3 GB RAM

llama.cpp serving (conflict resolver)

llama-server -m qwen3-1.7b-multitask-Q4_K_M.gguf -t 4 -c 2048

Per-pair conflict via /v1/completions (or /completion), temperature 0, cache_prompt, with a JSON grammar so the reply is strict yes/no:

root ::= "{\"conflict\": \"" ("true" | "false") "}"

(exact grammar used in production: root ::= "{" ws "\"conflict\"" ws ":" ws ("true"|"false") ws "}"). Prompt = "[conflict] existing: {existing}\nnew: {new}". Served figures: served Q4_K_M conflict F1 0.976 at 274 ms/pair (median, 4 threads), p90 358 ms -- the grammar-constrained decode beats the fp16 free-form parse (H3).

Files

model.safetensors -- merged fp16 model (load with transformers).
qwen3-1.7b-multitask-Q4_K_M.gguf -- 4-bit GGUF for llama.cpp.
adapter/ -- standalone LoRA adapter (apply on Qwen/Qwen3-1.7B).

License

Apache-2.0, inherited from the Qwen3 base. Causal News Corpus training text is CC0-1.0.

Downloads last month: 69

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Berk/reasongraph-extractor-1.7b

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(745)

this model