Text Generation
Transformers
Safetensors
Turkish
qwen3
turkish
türkçe
continued-pretraining
turkish-llm
türkçe-dil-modeli
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use ecloudtech/Erk-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ecloudtech/Erk-32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ecloudtech/Erk-32B") 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("ecloudtech/Erk-32B") model = AutoModelForCausalLM.from_pretrained("ecloudtech/Erk-32B", 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 ecloudtech/Erk-32B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ecloudtech/Erk-32B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ecloudtech/Erk-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ecloudtech/Erk-32B
- SGLang
How to use ecloudtech/Erk-32B 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 "ecloudtech/Erk-32B" \ --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": "ecloudtech/Erk-32B", "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 "ecloudtech/Erk-32B" \ --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": "ecloudtech/Erk-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ecloudtech/Erk-32B with Docker Model Runner:
docker model run hf.co/ecloudtech/Erk-32B
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9c90564 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | """Ek tur sonrasi olcek taramasi — esli bootstrap guven araliklari.
Soru-bazli vektorler ayni sorular uzerinde oldugu icin fark ESLI hesaplanir:
her yeniden orneklemede ayni soru indeksleri iki kosula da uygulanir. Bagimsiz
bootstrap'a gore araliklar daha dar ve dogru olur (soru zorlugu ortak varyans).
10.000 yeniden ornekleme, tohum 0 — OLCUM-PROTOKOLU ile ayni.
"""
import json, numpy as np
A = json.load(open("/ari/users/ytanis/olcek_ince_sft2.json")) # ek tur SONRASI
try:
B = json.load(open("/ari/users/ytanis/olcek_ince.json")) # ek tur ONCESI
except Exception:
B = {}
rng = np.random.default_rng(0)
N = 10000
def ci(x, y):
x = np.asarray(x, float); y = np.asarray(y, float); n = len(x)
assert n == len(y)
idx = rng.integers(0, n, size=(N, n))
d = (x[idx].mean(1) - y[idx].mean(1)) * 100
lo, hi = np.percentile(d, [2.5, 97.5])
return (x.mean() - y.mean()) * 100, lo, hi
def satir(ad, x, y):
f, lo, hi = ci(x, y)
anl = "ANLAMLI" if (lo > 0 or hi < 0) else " - "
print(" %-40s %+6.2f [%+6.2f, %+6.2f] %s" % (ad, f, lo, hi, anl))
print("EK TUR SONRASI — olcekler arasi (esli, n_sohbet=329, n_kiyas=652)")
print(" SOHBET")
for a, b in [("0.80", "0.00"), ("1.00", "0.00"), ("0.40", "0.00"), ("0.80", "0.40"), ("0.80", "1.00")]:
satir("sohbet %s vs %s" % (a, b), A[a]["sohbet_vec"], A[b]["sohbet_vec"])
print(" KIYAS")
for a, b in [("0.40", "0.00"), ("0.80", "0.00"), ("1.00", "0.00"), ("0.40", "0.80"), ("0.40", "1.00")]:
satir("kiyas %s vs %s" % (a, b), A[a]["mmlu_vec"], A[b]["mmlu_vec"])
if B:
print("\nEK TUR SONRASI vs ONCESI — ayni olcekte (esli)")
for o in ["0.40", "0.50", "1.00"]:
if o in B:
satir("sohbet %s: sonra vs once" % o, A[o]["sohbet_vec"], B[o]["sohbet_vec"])
satir("kiyas %s: sonra vs once" % o, A[o]["mmlu_vec"], B[o]["mmlu_vec"])
else:
print(" (%s once dosyasinda yok)" % o)
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