Instructions to use Adi-Evolve/gemma-2-9b-linkscout-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Adi-Evolve/gemma-2-9b-linkscout-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Adi-Evolve/gemma-2-9b-linkscout-v1")
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("Adi-Evolve/gemma-2-9b-linkscout-v1")
model = AutoModelForCausalLM.from_pretrained("Adi-Evolve/gemma-2-9b-linkscout-v1", 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

vLLM

How to use Adi-Evolve/gemma-2-9b-linkscout-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Adi-Evolve/gemma-2-9b-linkscout-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Adi-Evolve/gemma-2-9b-linkscout-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Adi-Evolve/gemma-2-9b-linkscout-v1

SGLang

How to use Adi-Evolve/gemma-2-9b-linkscout-v1 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 "Adi-Evolve/gemma-2-9b-linkscout-v1" \
    --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": "Adi-Evolve/gemma-2-9b-linkscout-v1",
		"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 "Adi-Evolve/gemma-2-9b-linkscout-v1" \
        --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": "Adi-Evolve/gemma-2-9b-linkscout-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Adi-Evolve/gemma-2-9b-linkscout-v1 with Docker Model Runner:
```
docker model run hf.co/Adi-Evolve/gemma-2-9b-linkscout-v1
```

Adi-Evolve commited on Nov 16, 2025

Commit

a998233

verified ·

1 Parent(s): eea688d

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +47 -13

README.md CHANGED Viewed

@@ -1,21 +1,55 @@
 ---
-base_model: unsloth/gemma-2-9b-it-bnb-4bit
 tags:
-- text-generation-inference
-- transformers
 - unsloth
-- gemma2
-license: apache-2.0
-language:
-- en
 ---
-# Uploaded finetuned  model
-- **Developed by:** Adi-Evolve
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-2-9b-it-bnb-4bit
-This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+library_name: transformers
 tags:
 - unsloth
+- gemma-2
+- fact-checking
+- misinformation-detection
+license: gemma
 ---
+# gemma-2-9b-linkscout-v1
+This model is a fine-tuned version of `unsloth/gemma-2-9b-it-bnb-4bit` for fact-checking and misinformation detection.
+## Model Description
+- **Base Model:** unsloth/gemma-2-9b-it-bnb-4bit
+- **Fine-tuned for:** Fact-checking and bias detection
+- **Training Method:** LoRA/QLoRA with Unsloth
+- **Merged:** Yes (adapter merged with base model)
+## Usage
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="Adi-Evolve/gemma-2-9b-linkscout-v1",
+    max_seq_length=2048,
+    dtype=None,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+# Generate analysis
+prompt = """<start_of_turn>user
+Analyze this article for misinformation: [ARTICLE TEXT]
+<end_of_turn>
+<start_of_turn>model
+"""
+inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Training Details
+- Merged from LoRA adapter
+- Original base: unsloth/gemma-2-9b-it-bnb-4bit
+## Limitations
+This model should be used as a tool to assist in fact-checking, not as a sole source of truth.