Instructions to use fedric95/gemma-2-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fedric95/gemma-2-2b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="fedric95/gemma-2-2b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fedric95/gemma-2-2b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fedric95/gemma-2-2b-GGUF 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 fedric95/gemma-2-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fedric95/gemma-2-2b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fedric95/gemma-2-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fedric95/gemma-2-2b-GGUF: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 fedric95/gemma-2-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fedric95/gemma-2-2b-GGUF: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 fedric95/gemma-2-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fedric95/gemma-2-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/fedric95/gemma-2-2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use fedric95/gemma-2-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fedric95/gemma-2-2b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fedric95/gemma-2-2b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fedric95/gemma-2-2b-GGUF:Q4_K_M

SGLang

How to use fedric95/gemma-2-2b-GGUF 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 "fedric95/gemma-2-2b-GGUF" \
    --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": "fedric95/gemma-2-2b-GGUF",
		"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 "fedric95/gemma-2-2b-GGUF" \
        --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": "fedric95/gemma-2-2b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use fedric95/gemma-2-2b-GGUF with Ollama:
```
ollama run hf.co/fedric95/gemma-2-2b-GGUF:Q4_K_M
```

Unsloth Studio

How to use fedric95/gemma-2-2b-GGUF 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 fedric95/gemma-2-2b-GGUF 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 fedric95/gemma-2-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fedric95/gemma-2-2b-GGUF to start chatting

Docker Model Runner
How to use fedric95/gemma-2-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/fedric95/gemma-2-2b-GGUF:Q4_K_M
```

Lemonade

How to use fedric95/gemma-2-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fedric95/gemma-2-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2-2b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

fedric95 commited on Aug 31, 2024

Commit

82d5847

verified ·

1 Parent(s): c86d739

Upload ./gemma-2-2b-Q3_K_M.mmlu.txt with huggingface_hub

Browse files

Files changed (1) hide show

gemma-2-2b-Q3_K_M.mmlu.txt +1558 -0

gemma-2-2b-Q3_K_M.mmlu.txt ADDED Viewed

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+1309	33.15508021
+1310	33.20610687
+1311	33.25705568
+1312	33.30792683
+1313	33.35872049
+1314	33.33333333
+1315	33.38403042
+1316	33.43465046
+1317	33.48519362
+1318	33.45978756
+1319	33.51023503
+1320	33.48484848
+1321	33.45950038
+1322	33.43419062
+1323	33.40891912
+1324	33.38368580
+1325	33.43396226
+1326	33.40874811
+1327	33.45892992
+1328	33.50903614
+1329	33.48382242
+1330	33.53383459
+1331	33.50864012
+1332	33.55855856
+1333	33.60840210
+1334	33.65817091
+1335	33.70786517
+1336	33.68263473
+1337	33.73223635
+1338	33.70702541
+1339	33.75653473
+1340	33.80597015
+1341	33.78076063
+1342	33.75558867
+1343	33.73045421
+1344	33.70535714
+1345	33.75464684
+1346	33.80386330
+1347	33.85300668
+1348	33.82789318
+1349	33.80281690
+1350	33.77777778
+1351	33.75277572
+1352	33.72781065
+1353	33.70288248
+1354	33.67799114
+1355	33.72693727
+1356	33.77581121
+1357	33.82461312
+1358	33.87334315
+1359	33.92200147
+1360	33.89705882
+1361	33.87215283
+1362	33.84728341
+1363	33.82245048
+1364	33.87096774
+1365	33.84615385
+1366	33.89458272
+1367	33.94294075
+1368	33.91812865
+1369	33.96639883
+1370	33.94160584
+1371	33.98978848
+1372	33.96501458
+1373	33.94027677
+1374	33.91557496
+1375	33.96363636
+1376	34.01162791
+1377	34.05954975
+1378	34.10740203
+1379	34.15518492
+1380	34.13043478
+1381	34.10572049
+1382	34.08104197
+1383	34.12870571
+1384	34.10404624
+1385	34.07942238
+1386	34.12698413
+1387	34.17447729
+1388	34.14985591
+1389	34.19726422
+1390	34.24460432
+1391	34.29187635
+1392	34.26724138
+1393	34.24264178
+1394	34.21807747
+1395	34.26523297
+1396	34.24068768
+1397	34.28775948
+1398	34.33476395
+1399	34.38170122
+1400	34.42857143
+1401	34.40399714
+1402	34.37945792
+1403	34.35495367
+1404	34.33048433
+1405	34.30604982
+1406	34.28165007
+1407	34.32835821
+1408	34.37500000
+1409	34.35060326
+1410	34.39716312
+1411	34.44365698
+1412	34.41926346
+1413	34.39490446
+1414	34.37057992
+1415	34.41696113
+1416	34.39265537
+1417	34.36838391
+1418	34.41466855
+1419	34.46088795
+1420	34.43661972
+1421	34.41238564
+1422	34.38818565
+1423	34.36401968
+1424	34.33988764
+1425	34.38596491
+1426	34.43197756
+1427	34.40784863
+1428	34.38375350
+1429	34.35969209
+1430	34.33566434
+1431	34.31167016
+1432	34.35754190
+1433	34.33356595
+1434	34.30962343
+1435	34.28571429
+1436	34.26183844
+1437	34.23799582
+1438	34.28372740
+1439	34.32939541
+1440	34.30555556
+1441	34.28174879
+1442	34.32732316
+1443	34.30353430
+1444	34.34903047
+1445	34.39446367
+1446	34.37067773
+1447	34.41603317
+1448	34.39226519
+1449	34.43754313
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+1456	34.54670330
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+1458	34.49931413
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+1461	34.63381246
+1462	34.67852257
+1463	34.65481887
+1464	34.63114754
+1465	34.60750853
+1466	34.58390177
+1467	34.62849352
+1468	34.60490463
+1469	34.64942138
+1470	34.69387755
+1471	34.67029232
+1472	34.64673913
+1473	34.62321792
+1474	34.66757123
+1475	34.64406780
+1476	34.68834688
+1477	34.66486121
+1478	34.70906631
+1479	34.68559838
+1480	34.66216216
+1481	34.70627954
+1482	34.75033738
+1483	34.72690492
+1484	34.70350404
+1485	34.74747475
+1486	34.79138627
+1487	34.83523874
+1488	34.81182796
+1489	34.78844862
+1490	34.83221477
+1491	34.87592220
+1492	34.91957105
+1493	34.89618218
+1494	34.93975904
+1495	34.91638796
+1496	34.89304813
+1497	34.93653975
+1498	34.97997330
+1499	35.02334890
+1500	35.06666667
+1501	35.04330446
+1502	35.01997337
+1503	35.06320692
+1504	35.10638298
+1505	35.08305648
+1506	35.12616202
+1507	35.10285335
+1508	35.07957560
+1509	35.05632869
+1510	35.03311258
+1511	35.00992720
+1512	34.98677249
+1513	34.96364838
+1514	34.94055482
+1515	34.98349835
+1516	34.96042216
+1517	34.93737640
+1518	34.91436100
+1519	34.95720869
+1520	35.00000000
+1521	34.97698882
+1522	35.01971091
+1523	34.99671701
+1524	35.03937008
+1525	35.01639344
+1526	35.05897772
+1527	35.10150622
+1528	35.14397906
+1529	35.12099411
+1530	35.09803922
+1531	35.07511430
+1532	35.11749347
+1533	35.09458578
+1534	35.07170795
+1535	35.04885993
+1536	35.02604167
+1537	35.06831490
+1538	35.11053316
+1539	35.15269656
+1540	35.19480519
+1541	35.23685918
+1542	35.27885863
+1543	35.25599482
+1544	35.23316062
+1545	35.21035599
+1546	35.25226391
+1547	35.22947641
+1548	35.20671835
+ Final result: 35.2067 +/- 1.2143
+Random chance: 25.0000 +/- 1.1009