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Model: Lauarvik/NVIDIA-Nemotron-Nano-9B-v2-GGUF Source: Original Platform
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NVIDIA-Nemotron-Nano-9B-v2-bf16.gguf filter=lfs diff=lfs merge=lfs -text
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NVIDIA-Nemotron-Nano-9B-v2-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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NVIDIA-Nemotron-Nano-9B-v2-BF16.gguf
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NVIDIA-Nemotron-Nano-9B-v2-Q8_0.gguf
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185
README.md
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README.md
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---
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base_model:
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- nvidia/NVIDIA-Nemotron-Nano-9B-v2
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---
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Quantized version of [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://hf.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2)
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При квантизации использовалась imatrix, корпус текстов для неё был создан следующим образом:
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```python
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import json
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import re
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import hashlib
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import time
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from datasets import load_dataset
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OUT_PATH = "calib_nemotron.jsonl"
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TARGET_SAMPLES = 7000
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CHUNK_SIZE = 900
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MIN_LEN = 300
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MAX_LEN = 3000
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out = open(OUT_PATH, "w", encoding="utf-8")
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seen = set()
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written = 0
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# -----------------------
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# SAFE LOAD
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# -----------------------
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def safe_load(*args, **kwargs):
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for i in range(5):
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try:
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return load_dataset(*args, **kwargs)
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except Exception as e:
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print("retry", i, e)
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time.sleep(2)
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raise RuntimeError("failed to load dataset")
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# -----------------------
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# CLEAN
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# -----------------------
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def clean_text(txt: str) -> str:
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if not txt:
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return ""
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txt = re.sub(r"<[^>]+>", " ", txt)
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txt = re.sub(r"\s+", " ", txt).strip()
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if "\x00" in txt:
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return ""
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return txt
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# -----------------------
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# DEDUP
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# -----------------------
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def is_duplicate(txt: str) -> bool:
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h = hashlib.blake2b(txt.encode("utf-8"), digest_size=8).hexdigest()
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if h in seen:
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return True
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seen.add(h)
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return False
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# -----------------------
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# CHUNK
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# -----------------------
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def split_chunks(txt: str):
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for i in range(0, len(txt), CHUNK_SIZE):
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chunk = txt[i:i + CHUNK_SIZE]
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if len(chunk) >= MIN_LEN:
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yield chunk
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# -----------------------
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# WRITE
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# -----------------------
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def process_text(txt: str):
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global written
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txt = clean_text(txt)
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if not txt or len(txt) < MIN_LEN:
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return
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chunks = split_chunks(txt) if len(txt) > MAX_LEN else [txt]
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for chunk in chunks:
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if written >= TARGET_SAMPLES:
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return
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if is_duplicate(chunk):
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continue
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out.write(json.dumps({"text": chunk}, ensure_ascii=False) + "\n")
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written += 1
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# -----------------------
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# CHAT
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# -----------------------
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def handle_chat(ds, ratio):
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global written
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target = int(TARGET_SAMPLES * ratio)
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start = written
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for x in ds:
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if written - start >= target:
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break
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conv = x.get("conversations")
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if not conv:
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continue
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txt = "\n".join(
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f"{m.get('from','')}: {m.get('value','')}"
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for m in conv if m.get("value")
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)
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process_text(txt)
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# -----------------------
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# TEXT
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# -----------------------
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def handle_text(ds, field, ratio):
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global written
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target = int(TARGET_SAMPLES * ratio)
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start = written
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for x in ds:
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if written - start >= target:
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break
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process_text(x.get(field))
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# -----------------------
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# CODE
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# -----------------------
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def handle_code(ds, lang, ratio):
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global written
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target = int(TARGET_SAMPLES * ratio)
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start = written
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for x in ds:
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if written - start >= target:
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break
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if x.get("lang") == lang:
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process_text(x.get("content"))
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# =======================
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# DATASETS (ONLY SAFE ONES)
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# =======================
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print("chat...")
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ds = safe_load("teknium/OpenHermes-2.5", split="train", streaming=True)
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handle_chat(ds, 0.35)
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print("en text...")
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ds = safe_load("wikitext", "wikitext-103-raw-v1", split="train", streaming=True)
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handle_text(ds, "text", 0.25)
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print("ru fallback (wiki dump alternative)...")
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# безопасная замена RU:
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ds = safe_load("wikimedia/wikipedia", "20231101.ru", split="train", streaming=True)
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handle_text(ds, "text", 0.2)
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print("rust...")
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ds = safe_load("bigcode/the-stack-smol", split="train", streaming=True)
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handle_code(ds, "Rust", 0.1)
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print("python...")
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ds = safe_load("bigcode/the-stack-smol", split="train", streaming=True)
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handle_code(ds, "Python", 0.1)
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out.close()
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print("written:", written)
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```
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56
config.json
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config.json
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{
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"architectures": [
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"NemotronHForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_nemotron_h.NemotronHConfig",
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"AutoModelForCausalLM": "modeling_nemotron_h.NemotronHForCausalLM"
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},
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"bos_token_id": 1,
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"chunk_size": 128,
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"conv_kernel": 4,
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"eos_token_id": 12,
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"head_dim": 128,
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"hidden_dropout": 0.0,
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"hidden_size": 4480,
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"hybrid_override_pattern": "M-M-M-MM-M-M-M*-M-M-M*-M-M-M-M*-M-M-M-M*-M-MM-M-M-M-M-M-",
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"initializer_range": 0.02,
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"intermediate_size": 15680,
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"layer_norm_epsilon": 1e-05,
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"mamba_head_dim": 80,
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"mamba_hidden_act": "silu",
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"mamba_num_groups": 8,
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"mamba_num_heads": 128,
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"mamba_proj_bias": false,
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"mamba_state_dim": 128,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"mlp_hidden_act": "relu2",
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"model_type": "nemotron_h",
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"n_groups": 8,
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"num_attention_heads": 40,
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"num_hidden_layers": 56,
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"num_key_value_heads": 8,
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"num_logits_to_keep": 1,
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"num_query_groups": 8,
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"pad_token_id": 0,
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"rescale_prenorm_residual": true,
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"residual_in_fp32": false,
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"rms_norm_eps": 1e-05,
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"sliding_window": null,
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"ssm_state_size": 128,
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"tie_word_embeddings": false,
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"time_step_floor": 0.0001,
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"time_step_max": 0.1,
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"time_step_min": 0.001,
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"time_step_rank": 256,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_bias": false,
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"use_cache": true,
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"use_conv_bias": true,
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"use_mamba_kernels": true,
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"vocab_size": 131072
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}
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