Instructions to use Nanbeige/Nanbeige4.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanbeige/Nanbeige4.2-3B with Transformers:

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

pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4.2-3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.2-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanbeige/Nanbeige4.2-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanbeige/Nanbeige4.2-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanbeige/Nanbeige4.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanbeige/Nanbeige4.2-3B

SGLang

How to use Nanbeige/Nanbeige4.2-3B 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 "Nanbeige/Nanbeige4.2-3B" \
    --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": "Nanbeige/Nanbeige4.2-3B",
		"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 "Nanbeige/Nanbeige4.2-3B" \
        --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": "Nanbeige/Nanbeige4.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanbeige/Nanbeige4.2-3B with Docker Model Runner:
```
docker model run hf.co/Nanbeige/Nanbeige4.2-3B
```

leran1995

nielsr HF Staff commited on 6 days ago

Commit

451ed48

1 Parent(s): f67a83e

Add community evaluation results for CLAW-EVAL, GPQA, HLE, HMMT_FEB_2026, SWE-BENCH_PRO, SWE-BENCH_VERIFIED, TERMINAL-BENCH-2.0 (#7)

Browse files

- Add community evaluation results for CLAW-EVAL, GPQA, HLE, HMMT_FEB_2026, SWE-BENCH_PRO, SWE-BENCH_VERIFIED, TERMINAL-BENCH-2.0 (3ca73c921d7825d7083782ef2191c48267804e39)
- Update .eval_results/hmmt_feb_2026.yaml (79b12235552607b10a9f2278f733d414d18d51e4)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (7) hide show

.eval_results/claw-eval.yaml +7 -0
.eval_results/gpqa.yaml +7 -0
.eval_results/hle.yaml +7 -0
.eval_results/hmmt_feb_2026.yaml +7 -0
.eval_results/swe-bench_pro.yaml +7 -0
.eval_results/swe-bench_verified.yaml +7 -0
.eval_results/terminal-bench-2.0.yaml +7 -0

.eval_results/claw-eval.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: claw-eval/Claw-Eval
+    task_id: general
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 52.2

.eval_results/gpqa.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 87.4

.eval_results/hle.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: cais/hle
+    task_id: hle
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 17.8

.eval_results/hmmt_feb_2026.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: MathArena/hmmt_feb_2026
+    task_id: MathArena/hmmt_feb_2026
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 82.1

.eval_results/swe-bench_pro.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: ScaleAI/SWE-bench_Pro
+    task_id: SWE_Bench_Pro
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 46.9

.eval_results/swe-bench_verified.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: SWE-bench/SWE-bench_Verified
+    task_id: swe_bench_%_resolved
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 63.6

.eval_results/terminal-bench-2.0.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: harborframework/terminal-bench-2.0
+    task_id: terminalbench_2
+  source:
+    name: Model Card
+    url: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
+  value: 44.1