Instructions to use WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled with Transformers:

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

pipe = pipeline("text-generation", model="WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled")
model = AutoModelForCausalLM.from_pretrained("WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled

SGLang

How to use WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled 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 "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled
```

11-47 commited on 3 days ago

Commit

39db0df

verified ·

1 Parent(s): 0683bc5

Delete adapter_config.json

Browse files

Files changed (1) hide show

adapter_config.json +0 -45

adapter_config.json DELETED Viewed

@@ -1,45 +0,0 @@
-{
-  "alora_invocation_tokens": null,
-  "alpha_pattern": {},
-  "arrow_config": null,
-  "auto_mapping": null,
-  "base_model_name_or_path": "11-47/Gemini3.5-Code.Reasoner-2b",
-  "bias": "none",
-  "corda_config": null,
-  "ensure_weight_tying": false,
-  "eva_config": null,
-  "exclude_modules": null,
-  "fan_in_fan_out": false,
-  "inference_mode": true,
-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 8,
-  "lora_bias": false,
-  "lora_dropout": 0.0,
-  "lora_ga_config": null,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "peft_version": "0.19.1",
-  "qalora_group_size": 16,
-  "r": 16,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "v_proj",
-    "gate_proj",
-    "q_proj",
-    "up_proj"
-  ],
-  "target_parameters": null,
-  "task_type": "CAUSAL_LM",
-  "trainable_token_indices": null,
-  "use_bdlora": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false
-}